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    "\n",
    "# Support Vector Machines in Python for Engineers and Geoscientists \n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "#### Contacts: [Twitter/@GeostatsGuy](https://twitter.com/geostatsguy) | [GitHub/GeostatsGuy](https://github.com/GeostatsGuy) | [www.michaelpyrcz.com](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446)\n",
    "\n",
    "This is a tutorial for / demonstration of **support vector machine modeling in Python**.  We have included in our workflow some simple wrappers and reimplementations of GSLIB: Geostatistical Library methods** (Deutsch and Journel, 1997). Support vector machines are a powerful method for machine learning classification.  The support vector machine is a generalization of the maximal margin classifier that deals with cateogries that cannot be separated linearly.   \n",
    "\n",
    "This exercise demonstrates the support vector machine approach in Python with wrappers and reimplimentation of GSLIB methods.  The steps include:\n",
    "\n",
    "1. generate a 2D sequential Guassian simulation using a wrapper of GSLIB's sgsim method\n",
    "2. add a trend (to simplify the segmentation problem) and truncate to build a categorical, exhaustive truth model\n",
    "3. extract random samples from the truth model\n",
    "4. separate into training and testing (20%) datasets\n",
    "5. build support vector machine classifiers with simple linear and polynomial kernels\n",
    "6. tune the \"C\" coefficient for the polynomial model with k-fold cross validation\n",
    "7. compare the tuned, polynomial model with the simple linear kernel model with confusion matrices\n",
    "\n",
    "To accomplish this I have provide wrappers or reimplementation in Python for the following GSLIB methods.  Only the sgsim, locpix are used for this workflow:\n",
    "\n",
    "1. sgsim - sequantial Gaussian simulation limited to 2D and unconditional\n",
    "2. hist - histograms plots reimplemented with GSLIB parameters using python methods\n",
    "3. locmap - location maps reimplemented with GSLIB parameters using python methods\n",
    "4. pixelplt - pixel plots reimplemented with GSLIB parameters using python methods\n",
    "5. locpix - my modification of GSLIB to superimpose a location map on a pixel plot reimplemented with GSLIB parameters using Python methods\n",
    "5. affine - affine correction adjust the mean and standard deviation of a feature reimplemented with GSLIB parameters using Python methods\n",
    "6. varmap - vairogram map\n",
    "7. gam -regularly sampled data variograms\n",
    "8. gamv - irregularly sampled data variograms\n",
    "9. nscore - normal score transform (data transformation to Gaussian with a mean of zero and a standard deviation of one)\n",
    "\n",
    "These methods are all in the functions declared upfront. To run this demo all one has to do is download and place in your working directory the following executables from the GSLIB/bin directory:\n",
    "\n",
    "1. sgsim.exe\n",
    "\n",
    "The GSLIB source and executables are available at http://www.statios.com/Quick/gslib.html.  For the reference on using GSLIB check out the User Guide, GSLIB: Geostatistical Software Library and User's Guide by Clayton V. Deutsch and Andre G. Journel.\n",
    "\n",
    "I used this tutorial in my Introduction to Geostatistics undergraduate class (PGE337 at UT Austin) as part of a first introduction to geostatistics and Python for the engineering undergraduate students. It is assumed that students have no previous Python, geostatistics nor machine learning experience; therefore, all steps of the code and workflow are explored and described. This tutorial is augmented with course notes in my class.  The Python code and markdown was developed and tested in Jupyter. \n",
    "\n",
    "#### Project Goal\n",
    "\n",
    "Prediction of categories, low or high porosity, away from sample data values over the are of interest.\n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries.\n"
   ]
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   "source": [
    "import os                                                 # to set current working directory \n",
    "import numpy as np                                        # arrays and matrix math\n",
    "import pandas as pd                                       # DataFrames\n",
    "import matplotlib.pyplot as plt                           # plotting\n",
    "from sklearn.model_selection import train_test_split      # training and testing datasets\n",
    "from sklearn.metrics import confusion_matrix              # for sumarizing model performance\n",
    "import itertools                                          # assist with iteration used in plot_confusion_matrix"
   ]
  },
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   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  \n",
    "\n",
    "#### Declare functions\n",
    "\n",
    "Here are the wrappers and reimplementations of GSLIB method along with 4 utilities to move between GSLIB's Geo-EAS data sets and DataFrames, and grids and 2D Numpy arrays respectively and 2 utilities to resample from regular datasets.  Available GSLIB functions include:\n",
    "\n",
    "1. hist\n",
    "2. pixelplt\n",
    "3. locmap\n",
    "4. locpix\n",
    "5. vargplt\n",
    "6. affine\n",
    "7. nscore\n",
    "8. declus\n",
    "9. gam\n",
    "10. gamv\n",
    "11. vmodel\n",
    "12. sgsim\n",
    "13. visualize_model - to visualize the machine learning solution space\n",
    "14. plot_svc_decision_function - visualize the model with margins included\n",
    "15. plot_confusion_matrix - plot confusion matrix\n",
    "\n",
    "For now we embed the functions in the workflow below. In the future this will be turned into a proper Python package.  Warning, there has been no attempt to make these functions robust in the precense of bad inputs.  If you get a crazy error check the inputs.  Are the arrays empty and are they the same size when they should be?  Are the arrays the correct dimension?  Is the parameter order mixed up?  Make sure the inputs are consistent with the descriptions in this document."
   ]
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   "cell_type": "code",
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   "source": [
    "# utility to convert GSLIB Geo-EAS files to a pandas DataFrame for use with Python methods\n",
    "def GSLIB2Dataframe(data_file):\n",
    "    import os\n",
    "    import numpy as np  \n",
    "    import pandas as pd\n",
    "\n",
    "    colArray = []\n",
    "    with open(data_file) as myfile:   # read first two lines\n",
    "        head = [next(myfile) for x in range(2)]\n",
    "        line2 = head[1].split()\n",
    "        ncol = int(line2[0])\n",
    "        for icol in range(0, ncol):\n",
    "            head = [next(myfile) for x in range(1)]\n",
    "            colArray.append(head[0].split()[0])\n",
    "        data = np.loadtxt(myfile, skiprows = 0)\n",
    "        df = pd.DataFrame(data)\n",
    "        df.columns = colArray\n",
    "        return df\n",
    "    \n",
    "# utility to convert pandas DataFrame to a GSLIB Geo-EAS file for use with GSLIB methods\n",
    "def Dataframe2GSLIB(data_file,df):\n",
    "    colArray = []\n",
    "    colArray = df.columns\n",
    "    ncol = len(df.columns) \n",
    "    nrow = len(df.index)\n",
    "    file_out = open(data_file, \"w\")\n",
    "    file_out.write(data_file + '\\n')  \n",
    "    file_out.write(str(ncol) + '\\n') \n",
    "    for icol in range(0, ncol): \n",
    "        file_out.write(df.columns[icol]  + '\\n')  \n",
    "    \n",
    "    for irow in range(0, nrow):\n",
    "        for icol in range(0, ncol):\n",
    "            file_out.write(str(df.iloc[irow,icol])+ ' ')  \n",
    "        file_out.write('\\n')\n",
    "\n",
    "    file_out.close()    \n",
    "\n",
    "# utility to convert GSLIB Geo-EAS files to a numpy ndarray for use with Python methods\n",
    "def GSLIB2ndarray(data_file,kcol,nx,ny):\n",
    "    import os\n",
    "    import numpy as np  \n",
    "\n",
    "    colArray = []\n",
    "    if ny > 1:\n",
    "        array = np.ndarray(shape=(nx,ny),dtype=float,order='F')\n",
    "    else:\n",
    "        array = np.zeros(nx)\n",
    "        \n",
    "    with open(data_file) as myfile:   # read first two lines\n",
    "        head = [next(myfile) for x in range(2)]\n",
    "        line2 = head[1].split()\n",
    "        ncol = int(line2[0])          # get the number of columns\n",
    "        for icol in range(0, ncol):   # read over the column names\n",
    "            head = [next(myfile) for x in range(1)]\n",
    "            if icol == kcol:\n",
    "                col_name = head[0].split()[0]\n",
    "        for iy in range(0,ny):\n",
    "            for ix in range(0,nx):\n",
    "                head = [next(myfile) for x in range(1)]\n",
    "                array[ny-1-iy][ix] = head[0].split()[kcol]\n",
    "    return array,col_name\n",
    "\n",
    "# utility to convert numpy ndarray to a GSLIB Geo-EAS file for use with GSLIB methods\n",
    "def ndarray2GSLIB(array,data_file,col_name):\n",
    "    file_out = open(data_file, \"w\")\n",
    "    file_out.write(data_file + '\\n')  \n",
    "    file_out.write('1 \\n')  \n",
    "    file_out.write(col_name  + '\\n') \n",
    "    if array.ndim == 2:\n",
    "        nx = array.shape[0]\n",
    "        ny = array.shape[1]\n",
    "        ncol = 1\n",
    "        for iy in range(0, ny):\n",
    "            for ix in range(0, nx):\n",
    "                file_out.write(str(array[ny-1-iy,ix])+ '\\n')  \n",
    "    elif array.ndim == 1:\n",
    "        nx = len(array)        \n",
    "        for ix in range(0, nx):\n",
    "            file_out.write(str(array[ix])+ '\\n')             \n",
    "    else:       \n",
    "        Print(\"Error: must use a 2D array\")\n",
    "        return            \n",
    "    file_out.close()\n",
    "    \n",
    "# histogram, reimplemented in Python of GSLIB hist with MatPlotLib methods\n",
    "def hist(array,xmin,xmax,log,cumul,bins,weights,xlabel,title):\n",
    "    plt.figure(figsize=(8,6))\n",
    "    cs = plt.hist(array, alpha = 0.2, color = 'red', edgecolor = 'black', bins=bins, range = [xmin,xmax], weights = weights, log = log, cumulative = cumul)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel); plt.ylabel('Frequency')    \n",
    "    plt.show()\n",
    "    return\n",
    "   \n",
    "# pixel plot, reimplemention in Python of GSLIB pixelplt with MatPlotLib methods\n",
    "def pixelplt(array,xmin,xmax,ymin,ymax,step,vmin,vmax,title,xlabel,ylabel,vlabel,cmap):\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    plt.figure(figsize=(8,6))\n",
    "    im = plt.contourf(xx,yy,array,cmap=cmap,vmin=vmin,vmax=vmax,levels=np.linspace(vmin,vmax,100))\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    cbar = plt.colorbar(im,orientation = 'vertical',ticks=np.linspace(vmin,vmax,10))\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    plt.show()\n",
    "    return im\n",
    "\n",
    "# location map, reimplemention in Python of GSLIB locmap with MatPlotLib methods\n",
    "def locmap(df,xcol,ycol,vcol,xmin,xmax,ymin,ymax,vmin,vmax,title,xlabel,ylabel,vlabel,cmap):\n",
    "    ixy = 0 \n",
    "    plt.figure(figsize=(8,6))    \n",
    "    im = plt.scatter(df[xcol],df[ycol],s=None, c=df[vcol], marker=None, cmap=cmap, norm=None, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)    \n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    cbar = plt.colorbar(im, orientation = 'vertical',ticks=np.linspace(vmin,vmax,10))\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    plt.show()\n",
    "    return im\n",
    "\n",
    "def vargplt(lag,gamma,npair,vtype,name,xmin,xmax,ymin,ymax,sill,title,cmap):\n",
    "    plt.figure(figsize=(8,6))\n",
    "    marker = [\"o\",\"v\",\"s\",\"h\",\"^\",\">\",\"<\"]\n",
    "    im = 0\n",
    "    if type(lag)==type(list()):\n",
    "        if vtype==0:\n",
    "            im = plt.scatter(lag,gamma,s=None, c=npair, marker=None, label = name,cmap=cmap, norm=None, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "        else:\n",
    "            plt.plot(lag,gamma,'C3',lw=3,c='black')\n",
    "    else:\n",
    "        nvar = lags.shape[0]\n",
    "        for ivar in range(0, nvar):\n",
    "            if vtype[ivar]==0:\n",
    "                im = plt.scatter(lag[ivar],gamma[ivar],s=None,label = name[ivar],c=npair[ivar], marker=marker[ivar], cmap=cmap, norm=None, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "            else:\n",
    "                plt.plot(lag[ivar],gamma[ivar], 'C3', lw=3,c='black')\n",
    "    ixy = 0 \n",
    "    plt.title(title)\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)    \n",
    "    plt.xlabel('Lag Distance (m)')\n",
    "    plt.ylabel('Variogram')\n",
    "    plt.arrow(0,sill,xmax,0,width=0.002,color='red',head_length=0.0,head_width=0.0)\n",
    "    plt.legend(loc = 'lower right')\n",
    "    if im != 0:\n",
    "        cbar = plt.colorbar(im, orientation = 'vertical')\n",
    "        cbar.set_label('Number of Pairs', rotation=270, labelpad=20)\n",
    "    plt.show()\n",
    "    return im\n",
    "\n",
    "# pixelplt with location map superimposed, reimplementation in Python of a MOD from GSLIB with MatPlotLib methods\n",
    "def locpix(array,xmin,xmax,ymin,ymax,step,vmin,vmax,df,xcol,ycol,vcol,title,xlabel,ylabel,vlabel,cmap):\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step)) \n",
    "    plt.figure(figsize=(8,6))\n",
    "    cs = plt.contourf(xx, yy, array, cmap=cmap,vmin=vmin, vmax=vmax, levels=np.linspace(vmin,vmax,100))\n",
    "    im = plt.scatter(df[xcol],df[ycol],s=None, c=df[vcol], marker=None, cmap=cmap, norm=None, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.xlim(xmin,xmax-step)\n",
    "    plt.ylim(ymin+step,ymax) \n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    cbar = plt.colorbar(orientation = 'vertical',ticks=np.linspace(vmin,vmax,10))\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    plt.show()\n",
    "    return cs\n",
    "\n",
    "# affine distribution correction reimplemented in Python with numpy methods \n",
    "def affine(array,tmean,tstdev):    \n",
    "    if array.ndim != 2:\n",
    "        Print(\"Error: must use a 2D array\")\n",
    "        return\n",
    "    nx = array.shape[0]\n",
    "    ny = array.shape[1]\n",
    "    mean = np.average(array)\n",
    "    stdev = np.std(array)\n",
    "    for iy in range(0,ny):\n",
    "        for ix in range(0,nx):\n",
    "             array[ix,iy]= (tstdev/stdev)*(array[ix,iy] - mean) + tmean  \n",
    "    return(array)            \n",
    "\n",
    "# normal score transform, wrapper for nscore from GSLIB (.exe must be in working directory)(not used in this demo)   \n",
    "def nscore(x):\n",
    "    import os\n",
    "    import numpy as np\n",
    "    file = 'nscore_out.dat'\n",
    "    ndarray2GSLIB(x,\"nscore.dat\",\"value\")\n",
    "    \n",
    "    file = open(\"nscore.par\", \"w\")\n",
    "    file.write(\"                  Parameters for NSCORE                                    \\n\")\n",
    "    file.write(\"                  *********************                                    \\n\")\n",
    "    file.write(\"                                                                           \\n\")\n",
    "    file.write(\"START OF PARAMETERS:                                                       \\n\")\n",
    "    file.write(\"nscore.dat           -file with data                                       \\n\")\n",
    "    file.write(\"1   0                    -  columns for variable and weight                \\n\")\n",
    "    file.write(\"-1.0e21   1.0e21         -  trimming limits                                \\n\")\n",
    "    file.write(\"0                        -1=transform according to specified ref. dist.    \\n\")\n",
    "    file.write(\"../histsmth/histsmth.out -  file with reference dist.                      \\n\")\n",
    "    file.write(\"1   2                    -  columns for variable and weight                \\n\")\n",
    "    file.write(\"nscore.out               -file for output                                  \\n\")\n",
    "    file.write(\"nscore.trn               -file for output transformation table             \\n\")\n",
    "    file.close()\n",
    "\n",
    "    os.system('nscore.exe nscore.par')\n",
    "    file_in = 'nscore.out'\n",
    "    y,name = GSLIB2ndarray('nscore.out',1,nx,ny)\n",
    "    return(y)\n",
    "\n",
    "# cell-based declustering, 2D wrapper for declus from GSLIB (.exe must be in working directory)\n",
    "def declus(df,xcol,ycol,vcol,cmin,cmax,cnum,bmin):\n",
    "    import os\n",
    "    import numpy as np\n",
    "    nrow = len(df)\n",
    "    weights = []\n",
    "    file = 'declus_out.dat'\n",
    "    file_out = open(file, \"w\")\n",
    "    file_out.write('declus_out.dat' + '\\n')  \n",
    "    file_out.write('3' + '\\n')  \n",
    "    file_out.write('x' + '\\n') \n",
    "    file_out.write('y' + '\\n')\n",
    "    file_out.write('value' + '\\n')  \n",
    "    for irow in range(0, nrow):\n",
    "        file_out.write(str(df.iloc[irow][xcol])+' '+str(df.iloc[irow][ycol])+' '+str(df.iloc[irow][vcol])+' \\n')        \n",
    "    file_out.close()\n",
    "    \n",
    "    file = open(\"declus.par\", \"w\")\n",
    "    file.write(\"                  Parameters for DECLUS                                    \\n\")\n",
    "    file.write(\"                  *********************                                    \\n\")\n",
    "    file.write(\"                                                                           \\n\")\n",
    "    file.write(\"START OF PARAMETERS:                                                       \\n\")\n",
    "    file.write(\"declus_out.dat           -file with data                                   \\n\")\n",
    "    file.write(\"1   2   0   3               -  columns for X, Y, Z, and variable           \\n\")\n",
    "    file.write(\"-1.0e21     1.0e21          -  trimming limits                             \\n\")\n",
    "    file.write(\"declus.sum                  -file for summary output                       \\n\") \n",
    "    file.write(\"declus.out                  -file for output with data & weights           \\n\")\n",
    "    file.write(\"1.0   1.0                   -Y and Z cell anisotropy (Ysize=size*Yanis)    \\n\") \n",
    "    file.write(str(bmin) + \"                -0=look for minimum declustered mean (1=max)   \\n\") \n",
    "    file.write(str(cnum) + \" \" + str(cmin) + \" \" + str(cmax) + \" -number of cell sizes, min size, max size      \\n\")\n",
    "    file.write(\"5                           -number of origin offsets                      \\n\")\n",
    "    file.close()\n",
    "    \n",
    "    os.system('declus.exe declus.par')\n",
    "    df = GSLIB2Dataframe(\"declus.out\")\n",
    "    for irow in range(0, nrow):\n",
    "        weights.append(df.iloc[irow,3])    \n",
    "\n",
    "    return(weights)\n",
    " \n",
    "# regular grid variogram, 2D wrapper for gam from GSLIB (.exe must be in working directory)\n",
    "def gam_2d(array,nx,ny,hsiz,nlag,xlag,ylag,bstand):\n",
    "    import os\n",
    "    import numpy as np\n",
    "    \n",
    "    lag = []; gamma = []; npair = []\n",
    "    \n",
    "    ndarray2GSLIB(array,\"gam_out.dat\",\"gam.dat\")\n",
    "    \n",
    "    file = open(\"gam.par\", \"w\")\n",
    "    file.write(\"                  Parameters for GAM                                       \\n\")\n",
    "    file.write(\"                  ******************                                       \\n\")\n",
    "    file.write(\"                                                                           \\n\")\n",
    "    file.write(\"START OF PARAMETERS:                                                       \\n\")\n",
    "    file.write(\"gam_out.dat           -file with data                                      \\n\")\n",
    "    file.write(\"1   1   0             -   number of variables, column numbers              \\n\")\n",
    "    file.write(\"-1.0e21     1.0e21    -   trimming limits                                  \\n\")\n",
    "    file.write(\"gam.out               -file for variogram output                           \\n\")\n",
    "    file.write(\"1                     -grid or realization number                          \\n\")\n",
    "    file.write(str(nx) + \" 0.0 \" + str(hsiz) + \"  -nx, xmn, xsiz                           \\n\")\n",
    "    file.write(str(ny) + \" 0.0 \" + str(hsiz) + \"  -ny, ymn, ysiz                           \\n\")\n",
    "    file.write(\" 1   0.5   1.0        -nz, zmn, zsiz                                       \\n\")\n",
    "    file.write(\"1 \" + str(nlag) + \"   -number of directions, number of lags                \\n\")\n",
    "    file.write(str(xlag) + \" \" + str(ylag) + \" 0 -ixd(1),iyd(1),izd(1)                     \\n\")\n",
    "    file.write(\"1                     -standardize sill? (0=no, 1=yes)                     \\n\")\n",
    "    file.write(\"1                     -number of variograms                                \\n\")\n",
    "    file.write(\"1   1   1             -tail variable, head variable, variogram type        \\n\") \n",
    "    file.close()\n",
    "    \n",
    "    os.system('gam.exe gam.par')\n",
    "    reading = True\n",
    "    with open(\"gam.out\") as myfile:   \n",
    "        head = [next(myfile) for x in range(1)] # skip the first line\n",
    "        iline = 0\n",
    "        while reading:\n",
    "            try:\n",
    "                head = [next(myfile) for x in range(1)]\n",
    "                lag.append(head[0].split()[1])\n",
    "                gamma.append(head[0].split()[2])\n",
    "                npair.append(head[0].split()[3])\n",
    "                iline = iline + 1\n",
    "            except StopIteration:\n",
    "                reading = False   \n",
    "    \n",
    "    return(lag,gamma,npair)    \n",
    "\n",
    "# regular grid variogram, 2D wrapper for gam from GSLIB (.exe must be in working directory)\n",
    "def gamv_2d(df,xcol,ycol,vcol,nlag,lagdist,azi,atol,bstand):\n",
    "    import os\n",
    "    import numpy as np\n",
    "    \n",
    "    lag = []; gamma = []; npair = []\n",
    "    \n",
    "    df_ext = pd.DataFrame({'X':df[xcol],'Y':df[ycol],'Z':rand_sample[vcol]})\n",
    "    Dataframe2GSLIB(\"gamv_out.dat\",df_ext)\n",
    "    \n",
    "    file = open(\"gamv.par\", \"w\")\n",
    "    \n",
    "    file.write(\"                  Parameters for GAMV                                      \\n\")\n",
    "    file.write(\"                  *******************                                      \\n\")\n",
    "    file.write(\"                                                                           \\n\")\n",
    "    file.write(\"START OF PARAMETERS:                                                       \\n\")\n",
    "    file.write(\"gamv_out.dat                    -file with data                            \\n\") \n",
    "    file.write(\"1   2   0                         -   columns for X, Y, Z coordinates      \\n\")\n",
    "    file.write(\"1   3   0                         -   number of variables,col numbers      \\n\")\n",
    "    file.write(\"-1.0e21     1.0e21                -   trimming limits                      \\n\")\n",
    "    file.write(\"gamv.out                          -file for variogram output               \\n\")\n",
    "    file.write(str(nlag) + \"                      -number of lags                          \\n\")\n",
    "    file.write(str(lagdist) + \"                       -lag separation distance                 \\n\")\n",
    "    file.write(str(lagdist*0.5) + \"                   -lag tolerance                           \\n\")\n",
    "    file.write(\"1                                 -number of directions                    \\n\")\n",
    "    file.write(str(azi) + \" \" + str(atol) + \" 99999.9 0.0  90.0  50.0  -azm,atol,bandh,dip,dtol,bandv \\n\")\n",
    "    file.write(str(bstand) + \"                    -standardize sills? (0=no, 1=yes)        \\n\")\n",
    "    file.write(\"1                                 -number of variograms                    \\n\")\n",
    "    file.write(\"1   1   1                         -tail var., head var., variogram type    \\n\")\n",
    "    file.close()\n",
    "    \n",
    "    os.system('gamv.exe gamv.par')\n",
    "    reading = True\n",
    "    with open(\"gamv.out\") as myfile:   \n",
    "        head = [next(myfile) for x in range(1)] # skip the first line\n",
    "        iline = 0\n",
    "        while reading:\n",
    "            try:\n",
    "                head = [next(myfile) for x in range(1)]\n",
    "                lag.append(head[0].split()[1])\n",
    "                gamma.append(head[0].split()[2])\n",
    "                npair.append(head[0].split()[3])\n",
    "                iline = iline + 1\n",
    "            except StopIteration:\n",
    "                reading = False   \n",
    "    \n",
    "    return(lag,gamma,npair)    \n",
    "\n",
    "# irregular spaced data, 2D wrapper for varmap from GSLIB (.exe must be in working directory)\n",
    "def varmapv_2d(df,xcol,ycol,vcol,nx,ny,lagdist,minpairs,vmax,bstand,title,vlabel):\n",
    "    import os\n",
    "    import numpy as np\n",
    "    \n",
    "    lag = []; gamma = []; npair = []\n",
    "    \n",
    "    df_ext = pd.DataFrame({'X':df[xcol],'Y':df[ycol],'Z':rand_sample[vcol]})\n",
    "    Dataframe2GSLIB(\"varmap_out.dat\",df_ext)\n",
    "    \n",
    "    file = open(\"varmap.par\", \"w\")\n",
    "    \n",
    "    file.write(\"              Parameters for VARMAP                                        \\n\")\n",
    "    file.write(\"              *********************                                        \\n\")\n",
    "    file.write(\"                                                                           \\n\")\n",
    "    file.write(\"START OF PARAMETERS:                                                       \\n\")\n",
    "    file.write(\"varmap_out.dat          -file with data                                    \\n\")\n",
    "    file.write(\"1   3                        -   number of variables: column numbers       \\n\")\n",
    "    file.write(\"-1.0e21     1.0e21           -   trimming limits                           \\n\")\n",
    "    file.write(\"0                            -1=regular grid, 0=scattered values           \\n\")\n",
    "    file.write(\" 50   50    1                -if =1: nx,     ny,   nz                      \\n\")\n",
    "    file.write(\"1.0  1.0  1.0                -       xsiz, ysiz, zsiz                      \\n\") \n",
    "    file.write(\"1   2   0                    -if =0: columns for x,y, z coordinates        \\n\") \n",
    "    file.write(\"varmap.out                   -file for variogram output                    \\n\")\n",
    "    file.write(str(nx) + \" \" + str(ny) + \" 0 \" + \"-nxlag, nylag, nzlag                     \\n\")\n",
    "    file.write(str(lagdist) + \" \" + str(lagdist) + \" 1.0              -dxlag, dylag, dzlag \\n\")\n",
    "    file.write(str(minpairs) + \"             -minimum number of pairs                      \\n\")\n",
    "    file.write(str(bstand) + \"               -standardize sill? (0=no, 1=yes)              \\n\")\n",
    "    file.write(\"1                            -number of variograms                         \\n\") \n",
    "    file.write(\"1   1   1                    -tail, head, variogram type                   \\n\")\n",
    "    file.close()\n",
    "    \n",
    "    os.system('varmap.exe varmap.par')\n",
    "    nnx = nx*2+1; nny = ny*2+1\n",
    "    varmap, name = GSLIB2ndarray(\"varmap.out\",0,nnx,nny)               \n",
    "          \n",
    "    xmax = ((float(nx)+0.5)*lagdist); xmin = -1*xmax; \n",
    "    ymax = ((float(ny)+0.5)*lagdist); ymin = -1*ymax; \n",
    "    pixelplt(varmap,xmin,xmax,ymin,ymax,lagdist,0,vmax,title,'X','Y',vlabel,cmap)\n",
    "    return(varmap)  \n",
    "\n",
    "# regular spaced data, 2D wrapper for varmap from GSLIB (.exe must be in working directory)\n",
    "def varmap_2d(array,nx,ny,hsiz,nlagx,nlagy,minpairs,vmax,bstand,title,vlabel):\n",
    "    import os\n",
    "    import numpy as np\n",
    "     \n",
    "    ndarray2GSLIB(array,\"varmap_out.dat\",\"gam.dat\")\n",
    "    \n",
    "    file = open(\"varmap.par\", \"w\")\n",
    "    \n",
    "    file.write(\"              Parameters for VARMAP                                        \\n\")\n",
    "    file.write(\"              *********************                                        \\n\")\n",
    "    file.write(\"                                                                           \\n\")\n",
    "    file.write(\"START OF PARAMETERS:                                                       \\n\")\n",
    "    file.write(\"varmap_out.dat          -file with data                                    \\n\")\n",
    "    file.write(\"1   1                        -   number of variables: column numbers       \\n\")\n",
    "    file.write(\"-1.0e21     1.0e21           -   trimming limits                           \\n\")\n",
    "    file.write(\"1                            -1=regular grid, 0=scattered values           \\n\")\n",
    "    file.write(str(nx) + \" \" + str(ny) + \" 1  -if =1: nx,     ny,   nz                     \\n\")\n",
    "    file.write(str(hsiz) + \" \" + str(hsiz) + \" 1.0  - xsiz, ysiz, zsiz                     \\n\") \n",
    "    file.write(\"1   2   0                    -if =0: columns for x,y, z coordinates        \\n\") \n",
    "    file.write(\"varmap.out                   -file for variogram output                    \\n\")\n",
    "    file.write(str(nlagx) + \" \" + str(nlagy) + \" 0 \" + \"-nxlag, nylag, nzlag               \\n\")\n",
    "    file.write(str(hsiz) + \" \" + str(hsiz) + \" 1.0              -dxlag, dylag, dzlag       \\n\")\n",
    "    file.write(str(minpairs) + \"             -minimum number of pairs                      \\n\")\n",
    "    file.write(str(bstand) + \"               -standardize sill? (0=no, 1=yes)              \\n\")\n",
    "    file.write(\"1                            -number of variograms                         \\n\") \n",
    "    file.write(\"1   1   1                    -tail, head, variogram type                   \\n\")\n",
    "    file.close()\n",
    "    \n",
    "    os.system('varmap.exe varmap.par')\n",
    "    nnx = nlagx*2+1; nny = nlagy*2+1\n",
    "    varmap, name = GSLIB2ndarray(\"varmap.out\",0,nnx,nny)               \n",
    "          \n",
    "    xmax = ((float(nlagx)+0.5)*hsiz); xmin = -1*xmax; \n",
    "    ymax = ((float(nlagy)+0.5)*hsiz); ymin = -1*ymax; \n",
    "    pixelplt(varmap,xmin,xmax,ymin,ymax,hsiz,0,vmax,title,'X','Y',vlabel,cmap)\n",
    "    return(varmap)  \n",
    "\n",
    "\n",
    "# variogram model, 2D wrapper for vmodel from GSLIB (.exe must be in working directory)\n",
    "def vmodel_2d(nlag,step,azi,nug,nst,tstr1,c1,azi1,rmaj1,rmin1,tstr2=1,c2=0,azi2=0,rmaj2=0,rmin2=0):\n",
    "    import os\n",
    "    import numpy as np\n",
    "\n",
    "    lag = []; gamma = []\n",
    "    \n",
    "    file = open(\"vmodel.par\", \"w\")\n",
    "    file.write(\"                                                                           \\n\")\n",
    "    file.write(\"                  Parameters for VMODEL                                    \\n\")\n",
    "    file.write(\"                  *********************                                    \\n\")\n",
    "    file.write(\"                                                                           \\n\")\n",
    "    file.write(\"START OF PARAMETERS:                                                       \\n\")\n",
    "    file.write(\"vmodel.var                   -file for variogram output                    \\n\")\n",
    "    file.write(\"1 \" + str(nlag) + \"          -number of directions and lags                \\n\")\n",
    "    file.write(str(azi) + \" 0.0 \" + str(step) + \" -azm, dip, lag distance                  \\n\")\n",
    "    file.write(str(nst) + \" \" + str(nug) + \" -nst, nugget effect                           \\n\")\n",
    "    file.write(str(tstr1) + \" \" + str(c1) + \" \" + str(azi1) + \" 0.0   0.0   0.0 -it,cc,ang1,ang2,ang3 \\n\")\n",
    "    file.write(str(rmaj1) + \" \" + str(rmin1) + \" 0.0 -a_hmax, a_hmin, a_vert               \\n\")\n",
    "    file.write(str(tstr2) + \" \" + str(c2) + \" \" + str(azi2) + \" 0.0   0.0   0.0 -it,cc,ang1,ang2,ang3 \\n\")\n",
    "    file.write(str(rmaj2) + \" \" + str(rmin2) + \" 0.0 -a_hmax, a_hmin, a_vert               \\n\")\n",
    "    file.close()\n",
    "    \n",
    "    os.system('vmodel.exe vmodel.par')\n",
    "    reading = True\n",
    "    with open(\"vmodel.var\") as myfile:   \n",
    "        head = [next(myfile) for x in range(1)] # skip the first line\n",
    "        iline = 0\n",
    "        while reading:\n",
    "            try:\n",
    "                head = [next(myfile) for x in range(1)]\n",
    "                lag.append(head[0].split()[1])\n",
    "                gamma.append(head[0].split()[2])\n",
    "                iline = iline + 1\n",
    "            except StopIteration:\n",
    "                reading = False   \n",
    "    \n",
    "    return(lag,gamma)                \n",
    "               \n",
    "# sequential Gaussian simulation, 2D unconditional wrapper for sgsim from GSLIB (.exe must be in working directory)\n",
    "def GSLIB_sgsim_2d_uncond(nreal,nx,ny,hsiz,seed,hrange1,hrange2,azi,output_file):\n",
    "    import os\n",
    "    import numpy as np \n",
    "    \n",
    "    hmn = hsiz * 0.5   \n",
    "    hctab = int(hrange1/hsiz)*2 + 1\n",
    "    \n",
    "    sim_array = np.random.rand(nx,ny)\n",
    "  \n",
    "    file = open(\"sgsim.par\", \"w\")\n",
    "    file.write(\"              Parameters for SGSIM                                         \\n\")\n",
    "    file.write(\"              ********************                                         \\n\")\n",
    "    file.write(\"                                                                           \\n\")\n",
    "    file.write(\"START OF PARAMETER:                                                        \\n\")\n",
    "    file.write(\"none                          -file with data                              \\n\")\n",
    "    file.write(\"1  2  0  3  5  0              -  columns for X,Y,Z,vr,wt,sec.var.          \\n\")\n",
    "    file.write(\"-1.0e21 1.0e21                -  trimming limits                           \\n\")\n",
    "    file.write(\"0                             -transform the data (0=no, 1=yes)            \\n\")\n",
    "    file.write(\"none.trn                      -  file for output trans table               \\n\")\n",
    "    file.write(\"1                             -  consider ref. dist (0=no, 1=yes)          \\n\")\n",
    "    file.write(\"none.dat                      -  file with ref. dist distribution          \\n\")\n",
    "    file.write(\"1  0                          -  columns for vr and wt                     \\n\")\n",
    "    file.write(\"-4.0    4.0                   -  zmin,zmax(tail extrapolation)             \\n\")\n",
    "    file.write(\"1      -4.0                   -  lower tail option, parameter              \\n\")\n",
    "    file.write(\"1       4.0                   -  upper tail option, parameter              \\n\")\n",
    "    file.write(\"0                             -debugging level: 0,1,2,3                    \\n\")\n",
    "    file.write(\"nonw.dbg                      -file for debugging output                   \\n\")\n",
    "    file.write(str(output_file) + \"           -file for simulation output                  \\n\")\n",
    "    file.write(str(nreal) + \"                 -number of realizations to generate          \\n\")\n",
    "    file.write(str(nx) + \" \" + str(hmn) + \" \" + str(hsiz) + \"                              \\n\")\n",
    "    file.write(str(ny) + \" \" + str(hmn) + \" \" + str(hsiz) + \"                              \\n\")\n",
    "    file.write(\"1 0.0 1.0                     - nz zmn zsiz                                \\n\")\n",
    "    file.write(str(seed) + \"                  -random number seed                          \\n\")\n",
    "    file.write(\"0     8                       -min and max original data for sim           \\n\")\n",
    "    file.write(\"12                            -number of simulated nodes to use            \\n\")\n",
    "    file.write(\"0                             -assign data to nodes (0=no, 1=yes)          \\n\")\n",
    "    file.write(\"1     3                       -multiple grid search (0=no, 1=yes),num      \\n\")\n",
    "    file.write(\"0                             -maximum data per octant (0=not used)        \\n\")\n",
    "    file.write(str(hrange1) + \" \" + str(hrange2) + \" 1.0 -maximum search  (hmax,hmin,vert) \\n\")\n",
    "    file.write(str(azi) + \"   0.0   0.0       -angles for search ellipsoid                 \\n\")\n",
    "    file.write(str(hctab) + \" \" + str(hctab) + \" 1 -size of covariance lookup table        \\n\")\n",
    "    file.write(\"0     0.60   1.0              -ktype: 0=SK,1=OK,2=LVM,3=EXDR,4=COLC        \\n\")\n",
    "    file.write(\"none.dat                      -  file with LVM, EXDR, or COLC variable     \\n\")\n",
    "    file.write(\"4                             -  column for secondary variable             \\n\")\n",
    "    file.write(\"1    0.0                      -nst, nugget effect                          \\n\")\n",
    "    file.write(\"1    1.0 \" + str(azi) + \" 0.0 0.0 -it,cc,ang1,ang2,ang3                    \\n\")\n",
    "    file.write(\" \" + str(hrange1) + \" \" + str(hrange2) + \" 1.0 -a_hmax, a_hmin, a_vert     \\n\")\n",
    "    file.close()\n",
    "\n",
    "    os.system('\"sgsim.exe sgsim.par\"')       \n",
    "    sim_array = GSLIB2ndarray(output_file,0,nx,ny)         \n",
    "    return(sim_array)\n",
    "\n",
    "# extract regular spaced samples from a model   \n",
    "def regular_sample(array,xmin,xmax,ymin,ymax,step,mx,my,name):\n",
    "    x = []; y = []; v = []; iix = 0; iiy = 0;\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    iiy = 0\n",
    "    for iy in range(0,ny):\n",
    "        if iiy >= my:\n",
    "            iix = 0\n",
    "            for ix in range(0,nx):\n",
    "                if iix >= mx:\n",
    "                    x.append(xx[ix,iy]);y.append(yy[ix,iy]); v.append(array[ix,iy])\n",
    "                    iix = 0; iiy = 0\n",
    "                iix = iix + 1\n",
    "        iiy = iiy + 1\n",
    "    df = pd.DataFrame(np.c_[x,y,v],columns=['X', 'Y', name])\n",
    "    return(df)\n",
    "\n",
    "# extract random set of samples from a model   \n",
    "def random_sample(array,xmin,xmax,ymin,ymax,step,nsamp,name):\n",
    "    import random as rand\n",
    "    x = []; y = []; v = []; iix = 0; iiy = 0;\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax-1, ymin-1, -1*step))\n",
    "    nx = xx.shape[0]\n",
    "    ny = xx.shape[1]\n",
    "    sample_index = rand.sample(range((nx)*(ny)), nsamp)\n",
    "    for isamp in range(0,nsamp):\n",
    "        iy = int(sample_index[isamp]/ny)\n",
    "        ix = sample_index[isamp] - iy*nx\n",
    "        x.append(xx[ix,iy])\n",
    "        y.append(yy[ix,iy])\n",
    "        v.append(array[ix,iy])\n",
    "   \n",
    "    df = pd.DataFrame(np.c_[x,y,v],columns=['X', 'Y', name])\n",
    "    return(df)  \n",
    "\n",
    "def visualize_model(model,xfeature,yfeature,response,title,):# plots the data points and the decision tree prediction \n",
    "    n_classes = 10\n",
    "    cmap = plt.cm.RdYlBu\n",
    "    plot_step = 10.0\n",
    "    plt.figure(figsize=(8,6))\n",
    "    x_min, x_max = min(xfeature) - 1, max(xfeature) + 1\n",
    "    y_min, y_max = min(yfeature) - 1, max(yfeature) + 1\n",
    "    resp_min = round(min(response)); resp_max = round(max(response));\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),\n",
    "                     np.arange(y_min, y_max, plot_step))\n",
    "    z_min = round(min(response)); z_max = round(max(response))\n",
    "    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    cs = plt.contourf(xx, yy, Z, cmap=cmap,vmin=z_min, vmax=z_max)\n",
    "\n",
    "    im = plt.scatter(xfeature,yfeature,s=None, c=response, marker=None, cmap=cmap, norm=None, vmin=z_min, vmax=z_max, alpha=0.8, linewidths=0.3, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xfeature.name)\n",
    "    plt.ylabel(yfeature.name)\n",
    "    cbar = plt.colorbar(im, orientation = 'vertical')\n",
    "    cbar.set_label(response.name, rotation=270, labelpad=20)\n",
    "    #plt.show()\n",
    "    return(plt)\n",
    "    \n",
    "def plot_svc_decision_function(model,plt,xmin,xmax,ymin,ymax, plot_support=True): # modified from Jake VanderPlas's Python Data Science Handbook  \n",
    "    \"\"\"Plot the decision function for a 2D SVC\"\"\"\n",
    "    xlim = [xmin,xmax]\n",
    "    ylim = [ymin,ymax]\n",
    "    \n",
    "    # create grid to evaluate model\n",
    "    x = np.linspace(xlim[0], xlim[1], 30)\n",
    "    y = np.linspace(ylim[0], ylim[1], 30)\n",
    "    Y, X = np.meshgrid(y, x)\n",
    "    xy = np.vstack([X.ravel(), Y.ravel()]).T\n",
    "    P = model.decision_function(xy).reshape(X.shape)\n",
    "    \n",
    "    # plot decision boundary and margins\n",
    "    plt.contour(X, Y, P, colors='k',\n",
    "               levels=[-1, 0, 1], alpha=0.5,\n",
    "               linestyles=['--', '-', '--'])\n",
    "    \n",
    "    # plot support vectors\n",
    "    if plot_support:\n",
    "        plt.scatter(model.support_vectors_[:, 0],\n",
    "                   model.support_vectors_[:, 1],\n",
    "                   s=3, linewidth=15, facecolors='black');\n",
    "    plt.xlim(xlim)\n",
    "    plt.ylim(ylim)\n",
    "    plt.show()\n",
    "    \n",
    "def plot_confusion_matrix(cm, classes, # from scikit-learn docs on confusion matrix\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "os.chdir(\"c:/PGE337/Variogram\")                                   # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will have to update the part in quotes with your own working directory and the format is different on a Mac (e.g. \"~/PGE\"). \n",
    "\n",
    "##### Make a 2D spatial model\n",
    "\n",
    "The following are the basic parameters for the demonstration.  This includes the number of cells in the 2D regular grid, the cell size (step) and the x and y min and max along with the color scheme.\n",
    "\n",
    "Then we make a single realization of a Gausian distributed feature over the specified 2D grid and then apply affine correction to ensure we have a reasonable mean and spread for our feature's distribution, assumed to be Porosity (e.g. no negative values) while retaining the Gaussian distribution.  We then add a y-coordinate trend and then trauncate the continuous simulation into 1 - high porosity and -1 - low porosity. We are keeping this workflow simple. *This is our exhaustive, truth model that we will sample*.  We added the trend to create a region of low at the top of the model and a region of high at the base of the model.  The resulting truth data set can be segmented with linear and low order polynomial kernels.  \n",
    "\n",
    "The parameters of *GSLIB_sgsim_2d_uncond* are (nreal,nx,ny,hsiz,seed,hrange1,hrange2,azi,output_file).  nreal is the number of realizations, nx and ny are the number of cells in x and y, hsiz is the cell siz, seed is the random number seed, hrange and hrange2 are the variogram ranges in major and minor directions respectively, azi is the azimuth of the primary direction of continuity (0 is aligned with Y axis) and output_file is a GEO_DAS file with the simulated realization.  The ouput is the 2D numpy array of the simulation along with the name of the property."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx = 100; ny = 100; cell_size = 10                               # grid number of cells and cell size\n",
    "xmin = 0.0; ymin = 0.0;                                          # grid origin\n",
    "xmax = xmin + nx * cell_size; ymax = ymin + ny * cell_size       # calculate the extent of model\n",
    "seed = 75075                                                     # random number seed  for stochastic simulation    \n",
    "range_max = 1000; range_min = 800; azimuth = 90                  # Porosity variogram ranges and azimuth\n",
    "mean = 10.0; stdev = 2.0                                         # Porosity mean and standard deviation\n",
    "#cmap = plt.cm.RdYlBu\n",
    "vmin = 4; vmax = 16; cmap = plt.cm.plasma                        # color min and max and using the plasma color map\n",
    "\n",
    "# calculate a stochastic realization with standard normal distribution\n",
    "sim,value = GSLIB_sgsim_2d_uncond(1,nx,ny,cell_size,seed,range_max,range_min,azimuth,\"simulation\")\n",
    "sim = affine(sim,mean,stdev)                                     # correct the distribution to a target mean and standard deviation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's add a y-direction trend and truncate into 2 categoeries -1 and 1, where 1 is high porosity and -1 is low porosity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GsXHJuGbP+ceKEbVLLc/xZnHXtNCiS86MU6RkHsP1Zi7PnHCy31YS+QorpZTqcBogXEAc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24835976cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x24838c68390>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csim = np.zeros((nx,ny))                                         # declare a new 2D array\n",
    "threshold = 10.0; trend_range = 10                               # set a threshold and trend magnitude\n",
    "for iy in range(0, ny):                                          # add trend and stochastic residual and truncate\n",
    "    for ix in range(0, nx):\n",
    "        sim[ix,iy] = sim[ix,iy] + (ix-50)/100 * trend_range \n",
    "        if(sim[ix,iy]<threshold):\n",
    "            csim[ix,iy] = -1\n",
    "        else:\n",
    "            csim[ix,iy] = 1    \n",
    "        \n",
    "pixelplt(csim,xmin,xmax,ymin,ymax,cell_size,-1,1,\"Truncated Porosity Realization\",\"X(m)\",\"Y(m)\",\"Categories\",cmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's extract some data samples from our truth model to train and test our support vector machine classification model and separate into training (80%) and testing (20%) subsets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R29omXLUeMW3tIUSyZINL+0O0biGSJmsvBHthj9OmzMmkSpYneoPTBhrDhzEy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krs2pQa+zYVXRlIWeAjhLCgigwkxfgCK9olSfEk/9SzKPliCNf5LAwrWY7npkPNOp7rkuZX3bQm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\nDh2LZrhv9PgIXn0plcWLrERFqdmzy8X/BodQo+al54GbtzRw7Kibx4YkU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B6PPplGy1aB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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2483cfa55f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0hb/FOKeIiIhAJLOYGCc1ahTdoOx8wx4RRsqJ089HZmYWhw4eIDMnkUfGzCIo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\nadfQCYKmaZqmadfQCYKmaZqmadf4PyCDeUIHiuAdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2483c63c6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2483d9b9f98>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_sample = random_sample(csim,xmin,xmax,ymin,ymax,cell_size,300,\"TPorosity\")\n",
    "train, test = train_test_split(rand_sample, test_size=0.2)\n",
    "locpix(csim,xmin,xmax,ymin,ymax,cell_size,-1,1,train,'X','Y','TPorosity','Training Data Truncated Porosity Realization and Random Samples','X(m)','Y(m)','Truncated Porosity',cmap)\n",
    "locmap(train,'X','Y','TPorosity',xmin,xmax,ymin,ymax,-1,1,'Training Dataset Truncated Porosity Samples','X(m)','Y(m)','Truncated Porosity',cmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the testing data that we will with hold.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VtwOIyCPAQmBjE+lvAR5uJPxiYJuq1nydKdQaVfUF9nWYxB2EURB6GKudg1R9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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2483d8c3588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BA2jbtq3XYW31008/cd+9j7NixVraZbbhuusuYf/99/M6LIeChHjQ6SqOZ34AVX0GZ7TN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\n/D/zst3Qn8DlEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2483cfa30f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x248384ae518>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locpix(csim,xmin,xmax,ymin,ymax,cell_size,-1,1,test,'X','Y','TPorosity','Testing Dataset Truncated Porosity Realization and Random Samples','X(m)','Y(m)','Truncated Porosity',cmap)\n",
    "locmap(test,'X','Y','TPorosity',xmin,xmax,ymin,ymax,-1,1,'Testing Dataset Truncated Porosity Samples','X(m)','Y(m)','Truncated Porosity',cmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's a good idea to look at the summary statistics of our training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>240.0</td>\n",
       "      <td>503.666667</td>\n",
       "      <td>287.173306</td>\n",
       "      <td>0.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>240.0</td>\n",
       "      <td>501.166667</td>\n",
       "      <td>288.061428</td>\n",
       "      <td>9.0</td>\n",
       "      <td>246.5</td>\n",
       "      <td>509.0</td>\n",
       "      <td>751.5</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TPorosity</th>\n",
       "      <td>240.0</td>\n",
       "      <td>-0.041667</td>\n",
       "      <td>1.001220</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count        mean         std  min    25%    50%    75%    max\n",
       "X          240.0  503.666667  287.173306  0.0  260.0  480.0  750.0  990.0\n",
       "Y          240.0  501.166667  288.061428  9.0  246.5  509.0  751.5  999.0\n",
       "TPorosity  240.0   -0.041667    1.001220 -1.0   -1.0   -1.0    1.0    1.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe().transpose()                               # calculate summary statistics for the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observed that the sampled data exist between 0 and 1,000 in both X and Y coordinates and that the truncated Porosity (TPorosity) variable is categorical -1 or 1.  It would be trivial to calculate the proportion of high ($prop_{high}$) and low ($prop_{low}$) porosity.    \n",
    "\n",
    "$m = prop_{high} \\times 1 + prop_{low} \\times -1$\n",
    "\n",
    "$prop_{high} = \\frac{m + 1}{2} = \\frac{-0.04 + 1}{2} = 0.48$\n",
    "\n",
    "$prop_{low} = 1 - prop_{high} = 1 - 0.48 = 0.52$\n",
    "\n",
    "We need to reformat our data to inputs to train the support vector machine.  Now we will extract the features (X and Y) into an array,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>320.0</td>\n",
       "      <td>369.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>140.0</td>\n",
       "      <td>759.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>50.0</td>\n",
       "      <td>599.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>800.0</td>\n",
       "      <td>209.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>370.0</td>\n",
       "      <td>289.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>150.0</td>\n",
       "      <td>299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>470.0</td>\n",
       "      <td>809.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         X      Y\n",
       "275  320.0  369.0\n",
       "276  140.0  759.0\n",
       "193   50.0  599.0\n",
       "255  800.0  209.0\n",
       "144  370.0  289.0\n",
       "75   150.0  299.0\n",
       "278  470.0  809.0"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X = train.iloc[:,0:2]\n",
    "train_X[:7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and the cateogrical response (truncated porosity) into separate arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TPorosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     TPorosity\n",
       "275        1.0\n",
       "276       -1.0\n",
       "193        1.0\n",
       "255        1.0\n",
       "144        1.0\n",
       "75         1.0\n",
       "278       -1.0"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_Y = train.iloc[:,2:3]\n",
    "train_Y[:7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train and plot linear and polynomial kernel support vector machine models over our solution space.  This will provide a spatial classification model for porosity low or high as a function of X and Y location.  We will start with a linear kernel.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM]"
     ]
    },
    {
     "data": {
      "image/png": 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ChgWLLcKN5fmhzj7k+zxbqWOHQt1SygW4oVxsSac+XuCYBRcwawHxlFfjRCXC\njoS6rSwtWuxskbfR8epOqDGdXc1T3QtGEJgpRk9qMLSYahTx7ECdq8IimylwdVyku5Lj0RbFrV9W\nsDlDOOLYs1aNrUVjl9JuHAkCKoNwVZTWnahIPGzzTK3WkvJ05IWqRiOOPbe2hzte+6rmZSogtkz7\nM6UsRF4lIntFZL+I3DnO+Q+IyKPp50kRiUSkNz13UESeSM89NMt3PysYjYDB0GJ2ezU2xyN3OJTF\n5nhNoQUKja35PE92eOx1Q4gTPxVrSw69Zv972/GcG7Keke7Fl5LhQM1lUwvMOZ5XKvCQujznQU+X\nQ3HbRl73G79CJtO8upN1bNYta97OBBGxgY8CrwCOAA+KyFdUdfeFNKr6p8CfpulfB7xPVc81/MxL\nVPVM0wo5Q4wgYJhVdrke573E1WEmm2x3dOZQvf1MrcZxNwIFKwPPK+Up2O29VUB1AtVcC2PR7CwV\nWiKEGGaJFtUdEaE3Y+OHEaUNq+jYsJqlK1c0Nc+wHvHcmaY6FLoJ2K+qBwBE5IvA64HdE6R/O/CF\nZhZotjFLA4ZZ45GKiz3osCkssikssqJS4J6BufP4tc+rMXRe2BQk+a+rFvj+gEus7R3dbWMhy3MS\njDhWV8XsEDVcjr68zYCOdI5R1YhyrjVr5nu9GpWBpA32P3qIwue/zaffe2diY9IsRLCs6X+AJSLy\nUMPnXaNyWAU81/D9SHpsnKJIEXgV8HcNhxX4log8PM5vtwVGI2CYFVSVQVfpl0tVKiNCZy3L6Xo4\nJ25VT7gRW+SSmtQSYUNQYG+txrZC++4WWOJkONYRsr/isTLOMkDEYC7ghR0mkpRhcrbm8/yoVGXA\nq7MsznBaQurFiNsLrVHnnHQjtjCyDS579CAPfe/epuZrzcyp2hlVvWGWivI64AejlgXuUNWjIrIM\n+FcReUpVvz9L+c0KRhBoQ1SVp32f836EZcM188B7YAxIPHYW0ofDyTCYG//q4wRf6hCbI5Hf/Lxn\nyLWlIn4h5tnAZ63jsNQxDlIWG9UoYrdXI46gL2ezKZeb0q6RWzpLDJXqHA1Crs5k6HYuCb2qyt5a\njcEgxrZhZ7P7kvHaoOVw6sDBpmU5B7sGjgJrGr6vTo+Nx9sYtSygqkfTv6dE5O9JlhqMIGCYnO8N\nVuj38qyVHJEqP3Bdnteba2tjLVsEMsooY3OOWj7XZedGx205wEiDZU4RsCI7P6p5zrK4us39HBia\nw8kwYPfvXjOeAAAgAElEQVT5kC1RAVuE826de4tVXtA1Na1Qp+3QWRhZz1WVfxussMorsFZs6nPR\nl4zTBk/aMXfcflNz8iPxLLimicaCwIPAFhHZQCIAvA14x+hEItIFvAh4Z8OxEmCp6nD6/yuB329m\nYafD/OghFxHP+j7LvDxdqYrdFmF7XGJ3xeWO7vYVBCCxLH9m0GNjnEdEOKEB+TKU7bmpZlvLWXaF\n7sXO9JzWGSqGXJc1KnZDe7O3EnJ1XLy4c6RHHAIv5mghYNU0BelnfJ9+L0+nJMayzhz0JVeVM+wK\nXbZGiUB7nojsG17Epquuakp+AEE94ujZ5nkWVNW6iLwX+CaJk/JPqeouEXl3ev7jadI3Av+iqo2F\nWQ78farZcYDPq+o/N62w08QIAm3G6aDOGimOOa7hOInbjA35HL1Onb2eR6ywOu+wOjv2XmabWhzz\nrO/T59jcsqTAk66XqFfzNndkjem7YR4wTjCs5WQ5ErjTFgTOBRFrZKwrSG1C4K0LbXBJxuGWJQV2\nuR7Zt7+U7bffxLU33zj7GY6g+VEEVfXrwNdHHfv4qO93AXeNOnYAuK6phZsFjCDQZhRswdOYwigX\nr9reO+Au0uU43NQxd9Xq8arLYAVWxTmOUGcgV+UFPWUyLXSzajBcMRZjVOrDGtHpTL/h5x3B15jc\nqL5ktveKPVZxGaombfAwdQZzNV7YU+ZF73/v7GY0ASKtdcm9EDCCQJuxvVDgW26Fa8LSRUOhY/is\nLppXNZoz9RBv2GIzORAokGWpn+GhYZdbTaQ8wzyiv2hzcjC4GC0zVuVg1uMVuekva20vFPhOtcLO\n+qW+5Cg+a2axLzldDwkqI9vgMj/Dg8MuL5q1XCYn41isXmLa+0wwo0ubYYtwR0+RxyoecR3UgjUF\nh/V5E+1lNAe8gDU60iOfI0IYTHyNwdCObC3keUZqPOO5SJw4w3pRuTSjWBOOCLf1Fng87UuwYE3R\nYV1u9vqSZ72ANaM8G851GwzrMcfOuXOX4QLECAJtSMm2ua3LSLiXQyTZrTRGeWq0hIsGVeWhikvV\nI3HbklGuLmfpT9fVn6h6nHVjiEEcWFdqX6F6Uz5/0S3wuXrIfYMuGggI5PNwc0fxilXgZdvhtq7m\ndfMiifZiTLnmuA0220ZgoWMEAcO8ZXshz0NVj616aUYyrBEdedMpLBYeqXr0VHKsTi3jCWDXQJW+\npRkO+D4M21xFOrqGcGDQo9MJ23orbqzKw+dr7IzSZQEFvxrzAC63tNmS17ZCjoerNbbqpW2vwxrR\nNYdtUASsKQYPMoyPEQQM85aSbbO+x2HfsIuEQnQhfG+x+TsVDO3BcE1ZISN1QpuiArs9j+GasomR\ns/8NcZ59rsctne0rCOyt1VhfH7nklROLWg3obFmxxqVsO6zvjthbcbFa1gbFhCGeIUYQmCGHfJ/n\nvPrFdb3nlwtkR1vptiG1OOaRBjuE9UWH1dn2VJlOxrpsjnV9Oeqq2DCjNVXD/EPGcWGfRQhiHVc7\nLSJNDcijqjzmelT8JP+uvMXO4pU5iarFypJ5FAZmXS7Hulzr2mDWsVjV116akvmGEQRmwDO1GucH\nYFNqLBP5yneDKq/oLbf1gBSr8r3zVXYGpYtre0dqPvVuv23XTy/HXEY4NLQPVkaJ6yPXqA+Jz/Z8\njr34hH5MpkEwP0PI8nzzur37hl2WVXMsT7UUg36dB6MqN3ZMfaDaVshzf2Wkul1VsTLtHTyrVW0w\njGKOn/dakvdCYf6InW3I0WrEqgbVoy3C+qDAPr+9fdvv9mpsDgojOs/V5HjObYKnEYOhiVzfUWB3\ntso5DQlV2Y9HqUPpchyeXyryTN7lhAZEqhyiRrUUsmEWreYbceMY9YRSw1JFlzi4HoRXEH0vb1ks\n77TYJy6BxgxonV2ZKtd15JtR7AWBWDLtj8FoBGbGREFu6u0tCLhRTJ+M46gkGntoPM7VQ572AgTY\nWshxPAwZDmMKjrCjUDDOPQwX8TVml1ujHinLc7O7dQ2gaNu8oq+DQ77PuajGDfk8hTSoji3CS3o6\nOBEGnAhrbMtmiVDuH64iwFWFHF3O7HWB56KQrtgZYzFfjGwqcUTPFbjavrqQZ0M+Zm+tRtmyeHm2\nvbWMB/waZ/yIjC3sLBbm1qGXND3o0ILHCAIzQMYJsHGGkGWZ9n6svRmb81qnR0aVcwr2U3s8j4FB\nWKcFVOHewSpFsdhmF3E14l+9Ci/uLY31ZmZYdJyphzx2zmdLVCQjwmk35IfFKrc1wfJ9XS7HugnO\n9Wey9Gey7PY8Bi/UXeAJt8ayzjpbC7Mz017uZDlgefTpyIZUtSO6rCsXgHJicW2h/Q1fvzdYYYmb\nZY3k8DXmu26Fm3rmLniWIGbXwAxp7xGrzdlcyrAvcNkcJ7PgYY04W/C5JtfeIWQ35XJ8J18hWytQ\nEptYlX22x47S5D7NI1VODMdcTRIcxYtjdmqJPeoRW0pRbHYEJR6peNxyBWuihoXJ7mGf7XHp4gx5\nKRnqVeVEIaA/MzcRKS9QV+XUcMxVad0VYJMWeKrisiWvszLbzojQVYITwwH9ktzfUXyWlq0FqyU7\n4NdY4mbplUT4yYnFjnqJJyser5mjMmQcYeUcCh4LESMIzIAV2SylJRZ7XA+NoTNr8aJ8+0e6ExFe\n0l1mj1fjTOhjWXBjMXfZKIGn6iF9UQZFOakhtShmJTm6cRgiohsHW4RoHgRIahW1OOZYGLDMceYs\nKmPLqI8d/FZIlud8d84FgZP1gL4oM0ZtX67bDFyh2n4yrisXOZzxebw6hIiws5hnZW5u73UuOeOP\nDWwkIoknwzkijJQTg7W5y3ABssB7oubTaTvcPIdBdmYLS4QdV7itqct2eJQqh+sBKzSLhcVjVImB\nlY1uRs2qwLg8OFyl5gpL4wyPWSFS8Lm1o9jWa78zYpxAOlWNKNtzX0G6bYfDErBk1PpXzYopWbNX\nnhNBwNPDISvCAoqyJwrI2UJfGzswmgkZe/zARjrHr9jYCMwM02UbpkzRshggYidFlkqGfsmwgyJD\nRBd9JxySGhuLC7PTmwn7azUK1QybtUCXOGzUPEurOZ70Fu62p6VFi9NcUg+pKgcyHlfl5976vWTZ\nhPkIXy9Z+HoaYxd01vx+qCq7hwK210sslQzLJMv2eonHB9vbeHgm7CwWeNpx0YZdEScIWFWcu3Cp\nkhoLTvdjaJFGQETeB/wyiWuPJ4B/DxSBvwHWAweBt6jq+TT9bwG/RDK/+DVV/ebcl9pwuh6ylQJ1\nKya+0O4tWKkZHrcrFB2LDaUMK6YZP30hc8aPWDfKy11ZbE75Cu1vDzYtthUK7KXGftcFTRxu3V6+\ncn/5s8UdXSUetl1qQdLxlHLCraXZe/gn6yHd4djlh0JoMxxHdFjzJJb4FZAR4aaeAk82OCdbVbTZ\nNIfCXsa26O82NgIzYc4FARFZBfwasF1VPRG5G3gbsB34tqp+UETuBO4EflNEtqfndwArgW+JyFZV\nneJmN8Ns4YgQo5TskR1aCYtr+/KUFmBHN1tcwTbyOePHFZchTxPvfBllR0eOJbOswr6qkOeqafTR\nB2s+h6r1RPS3YUXRnrF1vyUywrHPwZrP98+5s5ZH0j7G7ilOfn5+zzyfrfk8V62j4zyrLsfh9u7W\nLY+GkXJyaOFqXeaCVi0NOEBBRByS+dAx4PXAZ9LznwHekP7/euCLquqr6rPAfuCmOS6vAeixHYay\n9RFqQFXFy0ZGCLgMa4oOx3VkbNZzhCwttOa5PVp1KQ1nuSoqsjUustUv8ch5n3obSCyn6yHHB2O2\nhmnZwiLDg8LhYPY6+2bkscTJMDhO+wiyEcVZtEOYa06FAScGY7aMeFbM6vuYKWZpYGbMee1U1aPA\n/wAOA8eBQVX9F2C5qh5Pk50Alqf/rwKea/iJI+kxQwu4sTvPvpzLATyeEY99OZebuozHs8uxOpsj\n1xXzlOVySGvstVzCcp2tLVgvBxjylM5RTqW21JNgPa3m6WrAeh35XFaSnVXPl/ublMcNXZfax/6L\n7WN+q62fccNxnlX7eCI1NgIzpxVLAz0ks/wNwADwtyLyzsY0qqoi44UTuexvvwt4F8Dyhb41awJ2\nux7n/UQ92Zu32FaY3U6o03Z4cW+ZWhwjQG4ez3Tmmh3FAtsKiqcxBWnx3vIJgvX4ces1AhO2/HE8\neU6bGeQRq/Ko6+H5ChasLlzymNjlLLz2MdH7kNl8HzPEuAqeGa0YLV8OPKuqpwFE5MvAbcBJEVmh\nqsdFZAVwKk1/FFjTcP3q9NgYVPUTwCcArs7lW9+jzTEPDFfprGTZmHoMHKjVeShyuaE8+9Zo+QXQ\nwbUCS0b6om9ZOTKK1kc60nlOArbkW2/o2ZG1GPYiOhqek68x+dzsdfYdWYuKF1EelUdhCnl8b7DC\nBq94ccvciVpAravGVQ32BQupfZSzFlUvGlFvZ/t9zISMbdFvtJIzohWCwGHgFhEpAh7wMuAhoAr8\nO+CD6d9/TNN/Bfi8iHyYxFhwC/DAXBe63fHjGN8VuhrcBneLwyk3ICzp3Pr+NrQ9P9GR5756lbVh\ngTIWh8UnV1Z6ndZ3qDsKee4Jq1TcDCskyylCzuZ9XlycPWddF/IoX2EeJ8KA7lp2xL75frLsq7rT\nMoqcD+wo5LknqFL2Lj2rc3mfF83i+5gJYRxzatg4FJoJcy4IqOr9IvIl4MdAHXiEZBZfBu4WkV8C\nDgFvSdPvSncW7E7T/6rZMTCWs1F93IAnHZHDUBzRt0iXSgzjU7YdXt5X5mnf53g9YkchR2eb1BER\n4YVdZU4UA474Lv1Zh+uys+u2e7p5HA1CVmhhTDtbyD2SiPDC7jInSs17HzNBYMG6cJ4rWtLyVfV3\ngd8dddgn0Q6Ml/6PgD9qdrnmM0udDPstj6WjAp4M23W6rNarew3th4i0zFhxKlwIFtROeazNZTlE\nMCL8OKQByBY4c/E+pocx+pspC2cha5GTEaFcSqIfXuAUAd0lwTHSssEwKyx1MnjFOsMNKoDD1Fhd\nar3dx2JFBGxLpv0xmFgD84ZqHDEcxyyznQnVYNeXizyb8TnouQCsLDisyy1Qt3UGQ4u4o7PEnkyN\ng4EPApuKGZa15Ux5ceBYwrKO9tVszQeMINDm1FW5d7BKrmZTUJs9tsvqDpstE3hA25DLsSF35bHP\nDQbD1BARthcLC9Y19HyjHitnqu3j3Gg+YgSBNufBYZeNXjGx+hdYHmd4eshjZTYa4+rXYDAYFhvG\nWHDmGBuBNif0GbP1b1OcZ7dntssYDAYDxrPgjDEagTbkbD1k93BAXIfTQZ3VElNocFCigKm/hnbk\nTD1kz3CAppHolhYtts+yd0uDoRHHslhaNsuhM8EIAm1GqMoj53x2xEmUNF9dgigGh4vCwDOWx02m\nczW0GYHGPHbOZ3tad4ngzGDIU9S4eoaRAw2GiajHylk3uHxCw4QYQaDNeNL12Bpdcliywy7wGC5x\nrKyQLMNOnTUd9ggNgcHQDjzp1tgSFUc421lChv2uy9VGbjU0icRGoNWlmN8sWEGgEsWcqYezHl99\ntvCiiMfdGnEdsGFnKUeH7VCPlUyD+1JLhOc5JXZZFTb02fRY2RH+4Q0Lk9P1kKerAcSQzwrXFQvY\nbf7ew3gCV9ZtGvVjdBu8ppSj3CbeFQ1XRrPbhoi8CvhzwAY+qaofHHX+xSRu8Z9ND31ZVX9/Kte2\nAwu21udii71nQ4IeZWW2vfb4+hpz7zmXbfUStgixKg/UXG7ozbMyl+FEJaBfLpVZVSlkLXpNJ7Uo\nOBz4HD0fs0GT/Wl+Lebf/Aov6ym3tRC4KudwqhqwjJF112pDWbwWJ21we72ElbbB+2suN/bl28bV\nsmFqiDQ3+qCI2MBHgVcAR4AHReQrqrp7VNJ7VPWnpnltS1nQNX6jFni66radIPB41ePqVAiAZNa/\nLSqyq+pxW1eJw6Uqx6rKSslR1YhnMh63dxjd6mLhYKXOFr20ST0nFiv9PM8GPhtz7bvWvjqb47li\nlWNVWClZKhrxbMbj9iZEv5wpj7se21IhAC61wSerHrd1LuhuccHhWMKS5hoL3gTsV9UDACLyReD1\nJPFvmnntnLHga7zUW12CsUR1xrj9FZGLgUtu7SxxuhhysOZSti1ekS+bfbKLiXEC2PSIw5EwYGOb\nG0ff2lniVCHgkJ/U3Ze3ad2N6mPVySKShDUzzCvqsXJuZsaCS0TkoYbvn0hD2l9gFfBcw/cjwM3j\n/M5tIvI4cBT4z6q66wqubSkLXhDQNrxD20k8BjYKA6pKY5j6pU6GpeU21Kkamo/NGGFgQOv0ZeaH\nA6llmWzbu9y1bYhURwgDqroIesSFyQyFzTOqesMMi/BjYK2qVkTkNcA/AFtm+JtzxoKu9gfEY1Op\n/QbTa4sFvu9VL65Pqip7bY/rS20+3TPMCetKDgfDGus1WQbwNeZorsbLsu0R/30hcG2pwD21kW1w\nj+1yY6l9l17miliV+2pV7veq7AlqFwWmbdk8NxdK3JovtZWWR6TpjoGOAmsavq9Oj11EVYca/v+6\niPyliCyZyrXtwIIVBHwrZmtfhqVtuGsgZ1nc0VvkcdcjqoNlw/NLk8eD9+MYV2O6LbutDcYMM2dd\nLkeuJ+CA6yIKuazwkuLcGAoORhFZkQW/PTVvWdzeW+SJtA2KDTeV8nQsckPBPb7Hh86f4qkg8Vya\nRViZyXAsCNgT1PhyZYBt2Tz/qWcZ23LtYbfkWEJfqakaqAeBLSKygWQQfxvwjsYEItIPnFRVFZGb\nSLz2ngUGLndtO7Bga33ZttpSCLhAwba5uaN02XSxKj8YqoJnkVeLasZnbdlhY95oDxYy/dks/XNo\n5Ho0CNg7FFAKHepWTJiNub27NP52wAVCcYptcLHwgFflztNHCVBWOxneWO7m1aUuOm2boSjiG9VB\n/r4ywJ6gxq+efI4PLl3FTYXWP796rJz3wssnnCaqWheR9wLfJFm4+5Sq7hKRd6fnPw68CXiPiNQB\nD3ibqiow7rVNK+w0WbCCwELhwYrLGrdATqzEc0Yd9g66rMhmFvyszTA3RKo8NRAkHgEFUIhqyo+G\nqrygyyxHLAb2+B53nj5Kh2VxZ18/N49S/3faNm/t7OXNHT3cX6vywbMnuPP0UT66fE3LNQMiYDfZ\no5Cqfh34+qhjH2/4/yPAR6Z6bbthRpI2p+Yn28ca2RwX2O15LSqRYaGxp1ZjQzSyM7dFqPsLVxtg\nuESsyofOnyJAubOvn1sLE+/0sES4tVDmzr5+ApQPnz9FrK33GGWJTPtjMBqBtkfGaWMCxK1ve4YF\ng057RqCqPFL1qPgKMdg5uLGjQFbMHGO+cF+tylNBjdVOhpvziaq/rso/RwUOZvqxUDYFJ3iFU7s4\ncN6cL7HaybAnqPGjWpXbCq3THAmYKIIzxAgCbU4mB2F1pOvWA1LjeQVjI2CYHa7OF7jHdrk6vuT4\nJ1bFzl5e2nyo4tJTybEi3fsaVZV76lVe1tvRtPIaZpf7vSoAbyx3Xxzo7466OLz0lVhW8l5/HIdU\nT3+Tn3GStJYIbyh385GB09zvtVYQsC2hp9C+9mDzASMItDk3dhS5N6qS821KanPWDlnVYRuf6IZZ\nwxFhY5fDU0NVeupZfGIquZDbOyc3BFNVKh6sbnCAYYvQ42c5EQb0t7kvAUPCnqBGVoRXl7sAGI4j\nDpS2kbVsYo04Ofg0y7u2sDe/iSB89KK25zWlLj4xcIbd6Q6DVhEpDPrGE9RMMKNJm+OI8OKeMsNx\nxFAUsdMpLrp1rUBjHq961NOtlteWCuTniaHkQd/nuJd0UquKDmuzlzQ5x4OAQ16IAn05m635ud3D\nfq4ess9NAhuVssIL+4qciuoUrQxd9uXLEgFWPLYuduNwvh7QbyZp84JIlZVOhs509j8Yx0TZHgBO\nDj7Ng8/ezY0b3kJPthvPV7Kp3Ndp26zMZIhabCNglgZmzvzoTQ10WDarMtlFKQR892yVZcMF1tWK\nrKwUuOesSyVq/xnAIxWXwfPC+lqR9bUiZ8/CE9XEyHOfV+PIuZh1XpH1XpHovM2PhqpzVrajQcCu\nsyFrq8lzLQ5m+f5QlRWZLF321DwYOiKQGTsIHLF8NubM0tV8wRbhWBgyFCXuLFfYDuXqMwAs79rC\njRvewvKuLXTXDtPZIIAPRRHHwrAtomIaY8GZYQQBQ1vzWNXj6vDSfnZbhB31Ik9U/RaXbHICjRmq\nKsu4NC3ulyzn3JhIlWPViNVcGiy7xUE9i6E5EnAOVEI2a+Gik6Ki2Cyp5TjkX9lz3dTh8JTlUldF\nVTlIjZ6ykJsnGhsDbMvmCVC+UR0Ekjb24uAZ6sNPI1j0d12FDu3jZeGhEU6tvl4dJEDpsWw+fO4k\nv3LiEP/h+EF+5cQhPnzuJF/96leJ47j5NyCJRmC6H4NZGpgXqCrPBD4D9Yit+ck9EC40ojpjnNqI\nSBJDvo05XQ/piTOJ3rKBct3hfFTHqo/tgFbEWY4EAdsLzX+/Ok5goyVkOBK6rLuC2fzqbI4lSzLs\n8jwihavyWXrb2JGXYSw3F0p8uTLA31cGeHNHD5YIz3diNtce5P7qE1got9oBJeeScBercvfweQB+\nWEs0WWO8EL7uddx444185CMf4Zprrmla+R0RevKLp09sBubptTnVKOIHAy5rgzwryPJkxSdfCri+\nDUO7NgOZIDiMtHn8nT47wwGrxlIdOShW7DqdVoHIDsYEFjopIVsyc9QkLcbkP6h1upwrf7B5y+L5\npdZ7mDNMj1vzJa7O5nkqqHF/rcqt6Q6ALsvmldYFj30jNTyfHTzLqVR7tcp2+JmOnoteCAfqdf6u\ncp7vLenmwQcf5I477uA973lP08ofqTLojyPZGqaM0d+1OY8Me+wMSnSJgyXCBs0TVizO1ZvnUrOd\nuLZUYI/jJpHhSISAp22Pq5vrW3zG5C2LTEEZbph6D2qdUkHIWhZLixZnuPQOPY3xC/U5m02vKTkc\n4dIyQKjKc9kaW8za/qLDEuH9PcvIInzw7Anu8yoTOgmKVbl76ByfHDoLwK91L+WvVmzgjmKZ/aHP\nNyqDfKU6CAif/vSn+drXvkZvby8f+9jHAJo0e5m+fYCxEUgwGoE2Jw4FESFSJUTJi8UazfJMzaN3\nEYQpLlgWt/bmebzqoWlwmGtK80P9fEtniScdjwPpuntv3uKGQtIX7iwV2G/XOOC5oFDMCS8ozt2s\nekM+R6bX54DrQgyZLLy4NHlgI19jBObMWVCsSk2VvOmwm862XIEPLl3FnaeP8oHTR1ntZHhDuZvX\nNMQa+Hoaa+BoOgl5U7mbt3T2UlflXypDBChLbYct2RzL7QzXX3895XKZT33qU7z2ta8FWNeMsie7\nBprxy4sHIwi0ORHK4/UkCl1WLTwrplcs8ovIyKVsO9zWOT+r6s5iYcJ50OZ8ns0tjHq7Optjdfby\nGoChqM6PB2vYoZXEIcjG3NhVoDTF3QXTYZfrcaoak48sfCumpyhct0iWw1rFTYUSH12+hg+fP8We\noMZHBk7ziYEzrMxkOBIGNJrlFEXYktadw35Ab5ChO8qArUgBNuSzlMvJEsOrXvUqNm/ezP79+5vy\nAm0LuoyNwIwwT6/NGbBCro47KIl9MSDMgzrET+ZMMBjD3PDggMe2oHRRW6C+8sCgy0t6m1MHjwQ+\ntSGLq0mlpBjOVkKedmpsmWNfC4uNrdk8f9C3gu94wzxa8zgfR0SqLLMz9No2vipPhz5v7ejhVaUu\nhqM6RwcjbtLO1Pc5VCsRj1ged6S/aVkW73nPe3j/+9/flDJHCsOBsRGYCUYQaHN6NUvWFmpxWtEF\nrrfK7Kv5XF8yMyRDczlXD+kMMiOWDESEXGDjxjHFJuhkD7t1NoxSo/SR4YDnssXIARMSq3Jfrcr9\nXpU9Qe2ike22bJ6bCyVuHRVRsJFnQ589fo3TUZ16ah9wU6HEG8rdOCL4GpNFeNfJw2RFeHNnL7YI\nT3k+G+MCSBKf4GGt8HwpU6mNtDH4xV/8Rd7//vc3zfPQIlKQNgUjCLQ5olC0rSSSdUqs2nJvXobF\nQaiKM3oPJGCrUKf96mCgMQ8Pe9QDQKCvYLG92NowuXPBHt/jQ+dP8VTq7nfMVr7KANuyed7TtYRu\n2+FkFHKyXuelxQ46bZswtUG6KptnueOw3M6MEPIuREAd7YVQ9dIO2Ye1wv+oH+cDzkp6dKSA2Nvb\nC9AU5x8CxoZkhhhBoM2xsxC5I7fPPWvVuNYEHTLMAcucDE9mqvTXR+7ScLN1Oq3mTM+X5G3OeSG9\ncskgtKIRnbnLd/bfO1dlW1C62F7Oh3UeU5frFrD27AGvyp2njxKgrHYyvLHczatLXXRYFgNRxL+4\nQ3xp+Dx7ghq/cfoILy92sD6TY5njEKbC3NZsnq3Zy79PW4RjQcBQFNFp22wsZHmmGrCWHM+XMh9w\nVnINRU7lRsYfOHfuHEDTOi2jEZgZRhBoc27qLHJPVKHDz9CBzQk7YHmHtaicChlah4iwvTPL7qEq\ny8IsCpzOBFzX1TxBdGs+z4PlKgNunaVxlrNWSFyMua0w+WB+yPdZEeRGCM09OOx1A1igbg72+B53\nnj5Kh2Xxgd7lbMzkOB3V+VGtysl6yPPyRd7a2ctrS1381fA5vjI8wL+5w3xkWTc78lcuHG3L5tkT\n1PhGdZC3dvayxMlwoqPOvopLv+botzIcznncUR75wO+66y4Y415rdrAtoTNr+sOZYJ5em5MR4aW9\nHZythwxGEbdni2M87RkMzWRFNkt/X2I5bglc45Qm3WY4G9zYUcItxRwPA3Y6GToaBN9YlT1ejeEw\nxrJgRzFPybY5U6/TzzjLAHPg5bYVuFHEfz93kgDlA73LORAG7A18qqqctYp02KWLfizKts27u5dy\nXa7AB04f5c8GTvO/l6+9YpX6eF4Id5YKBMWY54KALbZNrzNSsxDH8QU/Ak0hVqUSGmPBmWAEgXlC\nn6EktxgAACAASURBVJOhbx7snTcsTESENVPYajibFC2LTbmRg4qq8m8DFdbWCqwTm0iVBzyPa3qz\nbM7l2C0+6xl5jbVAernhOOJEPVnbPxmFPOF7/z977x0m51ne+3+et07fne1NXatq2XKRJTcsYwO2\nD8SQQCBwyKEkhPQTSnByriQkv1N8jCGVBEyAQIAQ4GeDMTYYY1zAtmyMLdsqVi/b2+zu1Lc+5493\ndjS72pVW2i7N97p0aWfm7fPO89zvfX+/35tDjkWbpnNtOEa1WqBbajwbu5pIoh1fCB4rDDAy8nNu\n1YJU/fZQlDZNZ59d4NlClmvD56b8mMqF0BCnf1dj+OEPf8ihQ4cAcjM5/zOhUhqYGSo2DBVUUMGS\nwSHLoqUQIlb0mFaFYIMf4bWMTZWmoUV9eqQNBMS215Qcq+NLL4D2pWTAczlon6q1P53P8GQuw1HH\nIq6o2EXC8Nti1ShCsNkI8VpoLWbVulLGRg/V8cvIJjJF1ZEiBG+NVQOwK3/u3S7PyYXQ93nooYf4\n4Ac/iBk4Vh4/5x1OA4LgPjjffxVUMgIVTBN9jk2347LSMKjSKrfNbEBKyRHbouBL1oVClZLPNJCy\nPZaJ0zMTY02otsWjdJg2xws5VAWujoTnROI4FxjyXI47Nr2eQ5/r4kiJELBcNzCFwpVmlG0hSCoq\nQgi+nU5hCMFtsSoAXGBYr8UAfOnRO3KQxqp2iCznUOZ5thb5nrdHq7h3eIC9dmHKYzkTputC+L71\n6zl06BCmafLAAw/wpje9qZIRWKSojOgVnBG+lDwxnCFRMKgnxD5hQdRiR/wCZV/NE4Zch1+mLNpc\nkzAKP1NzLE+orKkY5pwRYU2Qlz7hiTbHZS/bDIM2Y3H3osj5Pn1FCd8lZoiootLjOrxo5UgqKmuL\nNr2NmlaS7tVNCMAnSvk0IOKO4gK9Iwd5/ui32Lbq16nVY7SVyY8TqkqLrs9IgnwmF8Iux8FGwnA/\n27Zt47Of/Szbtm07732dDYoQxOarWdcFisrVq+CMeCGbY3UhUvKXX0GIoYzDEaPA6ilqghWcHS+P\nWmz2oiUe9QY/wv7RHCtMiVbJDEyJTeEwj+YyXOKcMsfpwGJ5dPEPZaO+x+5Cjl7PZcQLUvWqELRp\nOlFFZY1hssYwSxP/2TBRyieE4ArnBE/ba2isag+CgPhKVg88Rl1ZV8lRz6PLcVgzQ87HRjPM5xuX\n82zRxGhv0cRojWGyyQjx/m9+jdtuuw1ljjMyvoSsWyELzgSL/9dTwTljrGY3GyYbtn16k5kaoXO8\nkGN1xcrgvOBLibBP/26WeSaHrQLrQxe+Ac5UONu9qwrB65IRXsrm8V2QCqyMaNPqmTBf8KRk0HPp\n9Vz6XIflukG7EUIBTrg2DarOuuITf52qlerU0w0AxjBRygewU7OJDf2YPVod9Wis6N/D6zWHcuXe\nQ9kRbCSbpuEbcDYoQnBtODYp6fC6oNHQnEOIiqHQTFEJBC4gZD2PX4zmkcVJRjEk2xJhwjNoDiMn\n+X3JcjuxCs4LvsJpsjYLn9g8dfZbbCi/dyWgGpKrqyKEJnmaDKsq1yQWT2nKlxJFCHwp+VF2lL4y\nm96EqtJS/DumqLw7XjNr0svJpHwAV+k+V9EXLKRD+Y/Vl5LvZoZL618oqHAEZoZKIHABYddIng1W\npDTQ+AXJLplj5wyaw9SHVQYshzpOMa+PKRbrI4u7BruYoQiBbkqcnCwRBKWUdOsWNxsXZzOpye7d\nZ2d4784Vsr5XkvD1ui5hRfCmaBWKEIQVhfXq5Da9wKz6L0wl5TsTdhWydLgOG40QO0IXRiBQ4QjM\nHJWrd4FgxPOIWtq4gUYRAt1WyfoeUeX8sgLrQyFeied5LeeAB+iwLKpWPA1miB2JKLvIYlsCJCim\nZFs8POdGPYsRKc8lPum9q8xZY6PpQkpJ2vdJFLNqj+fSHLYDy3xdCOpVjeay38LOSHzejm1Myvf7\nvSe5a7CHO2ub2D5FYyFfSnYVstw12IOB4CPJhgsmne5LSa7CEZgRKoHABQLL99AnydfrUuDMsEHR\nlmgYosGgeC4TVdbz2J0tIJ2glrssorHCXBy1XCklL+XyZItd0uIhwaWRySfi8vNAgbZZOA9NCK6r\nigVlFmb3SXGpoNu2OZxzyDs+I55PlaIRLwtYVamUUuzzBbeo3+91naDG7zk4UvJfE7XoQrBcM6hT\nNZpUnaSqLrgOfbpSvu9mhulwnUD/X9/KRvPC4aEETYcW+iiWNiqBwAWCek3nFT1L84TmMFndpVqd\nHXb/uUxWnpT8LJUbx+7utCz8aotVoYUPBp5O52jKmjQVjWkytscuL8eOCbXnqc5DVlusnIXzuBgD\nAAiCgOMpjzUyAhJGpMsBr8AlIlwizeV1l8Qs3btTwfJ9ej2XBlUjpChBmr1otFOtqqzUTBrLZHur\nFxEpcQzTkvIRkAs/kmy4oIKAMVwo2Y2FQiUQuEAghGBDwmDvSJZlbggfSadmsSmxMLX8vfk87U5k\n3A+0FZNDudyCBwJ534e8ICJOPX3GhEp3XuDE5ThjnzOdx2wEAhcrjuQcVsti0xsBYVVhlWvyqp9j\nrRKiU7fYPAf3riV9Tjo2vcWn/lRRxndTJM5qw2SFbhBXFBpUnfASMSKCs0v5toej7JiibLDUIYS4\nIM9rPlEJBC4gtBoGzXU6h20LHbjJWLgfft6T1E3GgF8EpbyU5xL3tdOUD1FfJeN7JMsa3JzreThS\n8mouj+1KEobC+lCoMkhNhgmKCUNRSOoKxwwfPebNyr3rS8mQ79HnOlSpKq2ageVLnshlMISgQdNY\nrZs0ajr1xe88rqjjyhNLCWeS8l3IUICItnSCtsWISiBwgUERgvZFYPRTZ6ikMi5JMf4WE4uAY9ig\n6RxUc9T74w9mVHWpUsY/hdaew3lkPY+nh3K0uxFMoZDOefzEzHBzMlYJBiZAqKdzTvJ4LA/rUzav\nmQ6klOy28vR4Dv2uW/Lj32SGaNWCp/23xaupVtTKd3KBwAcK3gXaYnKeUAkEZgm+lDxTTMvtK6bl\nVCHYWEzLXXOBpuWmwirD5PFwBi0fJl7sEveammNrbOHT6ZoQ1EQVOtMWrQTHcwKLxrhy2ne02jD5\n6TTPY3e2wGb3VIveuFBZbYXZk8+zJXLuvd8vZGyJmTxrZ1nvRtGFICc9jph5bj6Hp9m875ckfIKg\nVi6E4IhjIYA1hkmDqtGk6cSKT/lCCGrUyrB3oWGux1YhxK3A3wMq8K9SyrsmfP4e4BMEecY08LtS\nyt3Fz44V3/MAV0p51Zwe7Hmg8ouYBeyz8nw61cf+YhMPAxEQdWybfXaB+zLDFxRRR0rJcdtGAMsN\nY1LCmxCCnVUx9psFTtgWigrXRiKLpu66JRqmx7A5lg/6oKwO6zTop9ekhRDcNM3zkO7p5L+IUOl3\n5pf5vhQQVzVeVxfl1VwB15VEdMHN4ellTl62chywrXE2va1lEr47YtULzuavYP4gmNs2ukIIFfgs\n8AagA3heCPGAlHJv2WJHgRullCkhxG3AvcD2ss9vklIOzOFhzgiVQGCGeC6f5c7+TmwkbZrO22LV\n3FYm3Xk4O8L9mWH22YVA71vfytVL2NGr27bZO2rT7JhI4DE9y5Yqkwb99Fy5EIKN4TAs0tinSTdo\nmmTyn4hpn8dkVAIpWaIl5zmHKRSujE6eKfGkZKho09vrOgx4Lr8WT6IJgZRQpaglm95aVRvXn6ES\nBFx8mOOMwNXAISnlEQAhxDeBO4BSICClfLps+WeBtrk8oNlGJRCYgHNJ8e+z8tzZ30lcUSY180io\nKu9M1PCOeLJk5nFnfyefbVy2JDMDUkr2jdpsck81y6lxNfaMZGmoWwTF/wXGyojOcavACnmqxv2a\nmuPaSlngrLClj4JAE4IjtsVT+UzJQyCuqDRpOrYMGjJdFlq461kpAS4+KALCMyML1gkhflH2+l4p\n5b1lr1uBk2WvOxj/tD8RHwQeLnstgUeFEB7w+QnbXhSoBAJlOJcU/3ojxKdTfdhI7qxtOqO9pyIE\n14Rj3FnbxMf7O/lMqo/PNy5fcgPGgOeSdPTT2PYRRyt1QLuY0WoYyKTN4WwOPBAaXBELLZpyyGJC\nzvfpcZ1SjX/Id7k5EmeFblKlnnrab9JOt+ldKFxsJcClAinBnhlZcGC26vZCiJsIAoHry96+XkrZ\nKYRoAH4shNgvpXxyNvY3W6gEAkWca4r/vYka9tsF2jSd7UXP7iHP50FRT5fehC5t2q0O3qzlShP+\n9lCUNk1nn13g2UJ2ycl8dCFwOL3e7QmJKoJ07q50DrsASBCG5PJEiMRFRM5qMwzajMnLDYcLBTqy\nXsAlUKExqrIhvPAKj7mGlJJh30MA1arGiOfxnXQKCIibDarGVjNCVbGGUqtq0/LNn09cbCXA88GW\nW5tPe8+2bTo7OykUCnO3YzHnxlydwLKy123F98YfhhCXAv8K3CalHBx7X0rZWfy/TwhxP0GpoRII\nLDacT4r/y6PB9/y2WDWKEEgp+bqyjHzd9SgE9NA9/hZE/8O8RcsDQWbgrbFq/mm4n135pRcIVKsa\naTOPtE4RBKWUFMygl8HPR7K05UKltsWyIHnOz3JzTeyiddAbQ59jMzAiWTdmouNC74jNUcVi1SKx\nXZ5N9JY97fd6DpYvWWeY3BCJk1AUdoSjNKgaNWVteBcrLrYS4Lliy63NxP/3BxiMvp5sNkfnyU46\nTnYy8oUv0N3dje/7VFVVzekxzHF29XmgXQixiiAAeBfw7vIFhBDLgfuA90opD5S9HwUUKWW6+Pcb\ngb+Zy4M9H1z0gYAv5Xmn+AVwazQBwD5XMlq7FR3wpUfvyEEaq9o5YLSBf7C0jdujVdw7PMBeew4j\n5DnE9qowvxjJIeziZB/y2ZEII6XEtigFARBE6U22yQnbXjQ9BhYKh3MOK+X42nYjBkfyuSUfCFi+\nT5/nUpA+7cUe90/lM4x4HtWqygrNoFEL0vwQ3Bebl8gEOZPxYamWAKcLKSVtN9Vz6F2v5zvflvz8\nmb+jkB4BQFFVPnT71Vx33XUsX76ctrY2PvKRj8zJcShCENLm7hpLKV0hxB8APyKQD35JSrlHCPHh\n4uefA/4SqAX+ufjQMyYTbATuL76nAd+QUv5wzg72PLEggYAQopoghXIJAZHiA8BrwH8CK4FjwK9L\nKVPF5f+MoO7iAX8kpfzRbB3LM4XsaSn+Ed/nMT9OSqsi7mW4kWEa1FMT3PZQFB1wgD12gWvDMUal\nQFWDwa135CDPH/0W21b9Oo1ifJo4oaq06DrehGYqBwsF+vIeSAiZgssj4UU5gMRUjZ01sRKRa4yt\n7UuJMkmZLoxCznfm8xAXJ6ZSEC5RH5ROx+aYa9PnOqR8Dykhoiis1c1AOhqJExXKkudHTDY+eFLy\nmKvToddj+DZX+INsLBtJl3oJcLIUP4DnS3rzNh2ZAiczFh2ZAm5jPT/8x5P0Hd2HGa3FjLQSitah\n6dW8//3vmZfjlVJie3Mr0ZVSPgQ8NOG9z5X9/VvAb02y3hHgsjk9uFnAQmUE/h74oZTy7UIIA4gA\nfw78REp5lxDiTuBO4BNCiE0EqZjNQAsB+3KdlHJWzGrHGoyMpfhzvs8X1TU4ddsQQjAAHB15lfdb\nr1BfDAaUoilJr+fyVC7NteEYl2uSx9P7oWozjVXtQRBQ1U7TwE/HycpGPY8ux2FNWfOSV7J5SKus\nLprbWJbPU06WG6sX7wCiTQhSFCGQhgR7/HInVIvrF5DlvVhQbSqM5j0SZf0NLOkTNhdfsFcOX0pS\nvkev69DvuVwXjqEJQYfrcMS2aNA0VpXZ9I6VgOouEF7IxPEB4OteNZ0Nt6AqwTkez/eRGX2SbVow\nJC3VEuBYin8MluXQ0TXEiY5BTnQO0tGVwnFcAJLVUZav2cnDB0MIJUfbpvgClv8qvQZminn/tQoh\nqoDXAe8DkFLagC2EuAPYWVzsK8DjBE5NdwDflFJawFEhxCECssUzs3E8++wChhDcFgtqWE/4YZy6\nqxBClKX4N/JE/wneTrq03rZQlAezI+wqBIY0pqLwhsJefgwQ30BddDnhwWd4i+ynPBJ4KDuCjWST\ncYokNpDz2cCp16ZQiBQ0Bl2HWm3pyPIuSZi8mMqy0g1honBEydMaV8c18blYsSEU4ueRLKN5jRZp\n0IvDSNjmxsjinCR6XIfdVo6+MpveiKKQ9X2qVJXLQ2G2hSKzMgAvZknexPHhhOtxoupKDEUbVwJ8\nLt/KNnmitN5SKwFuubUZ+We/wT89t5If//RFMgM9ZEcGA0q+EESr6ojVXUK8tol4bROpcBRc6Np/\nECOUWNBjF6LShnimWIiwfRXQD3xZCHEZ8ALwx0CjlLK7uEwPQW0FAg3ns2XrdxTfOw1CiA8BHwJo\nnOYTiSclLZpOoshYHlWiiGKde1yKX41DWSBwQyTGg9kRUp4XpMWF4ErNZ7P9Mi/2vkJc+GzWFERZ\nScGXku9mhgHYXmQU+1KieKffxfVSp9uxl1QgUKfp3FyncaBQICUl14RPtZRdDPClZG++QM7zqdFV\n1pjmvD3FCCG4virGQNThhJWnxdC5Qo/Py77PhLzv01dG6rvcjNBWNFnK+D6rDZNGVaNR04kJpXS9\njFn6Xhe7JG/i+HDMV9HNOmD8+IAaB/fUelOVABcLxpQcva5L6NIYz69o5T8+8gt6Dj2CUFTMSA1m\npAEzWocZqcG1dYa7YLjLB7oW+vBPQyUQmBkWIhDQgCuAP5RS7hJC/D1BGaAEKaUUQpzzL6ho1HAv\nwAYzNK31VSHosu2SDr7eG+WI76IqWinF35BYS7L/4LirtVkPIQAHya5CtkQiCikK1xgQcErGY1ch\nS4frsNEIsaNYb1SEAE2OG0QAuoXNZmPpBAFjUIRgQ3jxEcHynseTqRxrnTC1QiUlXR4LZbipen4b\nAtVpOnULFNxJKfEIyjo53+fh7AjDJZteqFNPHVeTpvNr8eScHs9SkORNHB82qD4/y3eiRdrGlQCr\nh8YnKCcrAc4XJqb4AVzXo6snFaT5OwY52TVEPh/U8ZTkJTzTFcHKuTSt3YoRqkYsIW6HAIylc7iL\nEgsRCHQAHVLKXcXX3yEIBHqFEM1Sym4hRDPQV/x8WhrO88VGI8Q+u8DD2RHemajhBs1h7+BTpGtv\nKAUDxuBz3KykKZ/cf5gbRRIk/e8a7JlUVjQGX8qSrMhA8JFkw7jlWqIqJ0Yslhc5AkPSQcT8i0p/\nP9d4KVvgEufU95MUGnohzP58gU2RxRe4zAZ8KRnyPHo8pyjnc1mhG1wXjhEWgqSqsbZY36+bYNM7\n15iuJO/XYtV8ZXSQb4ym+Gh/B8s0naiizlvZYOL40KAqbEi/yH6tGs2I0VS1Hj9zmBvdrnGj6WQl\nwPnAWBDw7Z4bGejuYqCrg/6uTob6evDc4GkjkVxBfcu11LW0Ut/Sxq59o+T2HCJRN6+HOmuQSBx/\ncWZelgrmfaaRUvYIIU4KIdZLKV8DbibwbN4L/DfgruL/3yuu8gDwDSHEZwjIgu3Ac7N1PNvDUe7L\nDHN/Zph3xJPoQvA79PLzvu/Rr8ap8rK8Ti0QKjOML0/xf6Cqlq+ODPHx/k7aNJ23xqq5veyp5qHs\nCN/NDNPhOhgI7qpvPS3F2R4O0a87gSMdUGeqbA5dXGYkcw3fPV1rHBMqxx1rXo/DKjrq1c+BY54j\nJWnfK3XXuz8zXHrijysKrZpOS5mE7/WRhSlNTCXJk1LS6zoIoFE3TisbAPR4LrjuvJUNJo4PihC8\nTc3yytAPOKBWo/s214oM9dqZS4BzCSklGemT2F7NEzdezqc/uoejvywOn0JghpPFFH8TZrQWdSjE\n/sMQPGv1nWHLSweBe28F54uFeuT8Q+DrRcXAEeD9BA/X3xJCfBA4Dvw6QFGv+S2CQMEFfn+2FAMA\n14SibDBC7LcLpRS/LgQ7dRdIFXl+4wfs8hT/byZq2R6K8plUH/vsAv803M+9wwNBndNxsIu6sbMN\nWPWaTn3V0isFLBXISeZcX0rmMwP6QiZHLgu1vk6HUkAJS3bEI+fNU8iVteHt9RyGPJeQUHhXPIkQ\ngkvNMCqCRk0juog6H00myetzHF4Zsah1dCTwXWWAb1pDOMWywVtj1dyXTtHluXyytplBz52XssFk\n44MQgkt1uJThs44PO2YQ0G+5tZnYHetPe9/3JX3DeU70ZzjRn+Fkf4bRnE1+1Q387Hs2Qx0W1Y2b\nMaN1GJEkinJhZxYFEmWp6nAXCRbkDpFSvgRM5u188xTL/y/gf83FsShC8NFkQzCYnGeKf6MZ5vON\ny3m2yHzeW2Q+rzFMNhVTmDsqzUgWFCsjGh0FizZO1WwPqHm2ReanhnvYKmBkNFoIejUkpUY667FH\nK3DJNEoTY+SuPs+lXTdRhGC3lWOvVSjZ9F5mRmgoKye1z3NaerqYKMmTUvLqiFVqZnXIL/BNawhT\nCP53Xcu43+M/DffzipXnT2oa58XJbzbGh/NBuVuf4zh0d/XQcbKTzpOddHZ0Y1sWUEe8Kk7rJS1s\nWdbKf76sox4dpGHVxVcwF6ISCMwEF3aoOE1sNMPcVd/Knf2d553iV4Tg2nBsyWiGLza0GSZutcWh\nXNAQSNHhkphBbJ54GP0FjxWMDzriQuWIZQUuGpMg7XscdSx6XZc+z6FQrIPWxjTqNI2NRoi1urkk\nbHrLMVGS1+86pWZWtu/zd243DpL3qnVcYp4y1pooyZsvJ7/ZGB/OBatvbqDz/bfyZ58aZe/jf42V\nTwUyPkAPJTAjdYSijZjROlQ9gnhFABmAkuLpYoIQoFdkAzNCJRAo4upwlM82Lptxin8x4bhlcTzj\nIr2gyU1zRKV9Hpvc9DkO+zMWOAKpQE1YYUt0ZtfNl5LnMznGepgIXXJlPEx0Gp0PV4ZMVoYWxs73\nbCoyS/r0FVP8KzSDek1n1Pd4Pp+jSlVZXrTpbVR1EsV6RvUSJZNOlOQpQuAXf1/f8QfpxSGJyjoR\nQiH4zh/2whwyWjH1XgakR9b3iRavw3w4+c1kfJjKqQ+CTE/KcunIFOgouvV9r+Uqnrj7ML2HhzEi\nNSTq1hVr/LWo2uQNrS5mSClx/QpHYCZYmiPJHOFCSvEPug4dwz7tY/72PnSN2JxQLZbPg6TJkj6v\npCw2+dHS/lNplz0iz+YZsPR3pXM0Z0MlfwLpSp7xstxSu/Ca/DOhLaLRk7dpKrOc7pM2nYrF/Wm7\nZNOrCIgKhfripP8biZpF04Z3tjBRklen6bxsZGhxDLLFWu9/UZI4pk9UUfmeG2Fv/a14vkvW/RFq\nuJGv0cjv0A/Mn5Pf+YwP5Sl+AN/36evpo+NkJx0nu+g82Uk2G5RKQqEQbctaefZICGSBZZurEYuI\n27GYUSkNzAyVQGACLpQU/4GczSo/HIhsi2jB4EguNy+BwJ5cgXYvMm7/STQO5u0pU+Fng5QSq8A4\nkyIhBA22SYdt0TYH5zUbrne+lERVld2RDE/nRkn6Oq26TjQMnoSEorBSN2nUNOpVveTEqAkxr5K+\n+cJESR7AlVUhfjmaZZ+TR0OwPKyzrSqE5/s84Rr0dT5Kz/B+fOmRs4Z5IjtCkxjlLdE4ihDz5uR3\nLuPDllubMT/5m7zvqwnSAw+SHuwhM9SH7wa9N8xoInDqq1tPvLaJULyaYSHoO3wQM1pRDU0XAVmw\nkhGYCSqBwIUKOUWP7mLg7EjJS9kcjgNSQHtUp0GfvbSj68vJrYVnIPeVgPBP32YMlbQXmKN02zZH\ncw5I0HW4PBo578n0fF3vxpwmAZ7KpTnm2CWb3mREoU1TuS4SQRWCK+X5qwaWKiaT5FVpGjfVxPiK\nNUCr1Lm9NsE+K889Q7285hwct77t5TmReoVPAQ9mUqXrP9dOfmdK8Y8hbbt0ZCw6swV2XXor3/rD\nXXQfGADACFdjRuqC5jzRWpAR0gOQHgAYKP6r4FwhhECrZE5mhEogcIEioSukcx7xsiY3jvQxjGCi\n+ulQhg12tDRZH7YK2EmbNmN2goHlIZ2ujE1zWSrclxJ1BgpJRQik7gdtH8twUilwTSjMccuiZ9hn\nVbEc4hR8fmpnuCUZO+fJ9lxc736v9wQfTTbSqOn0eg4FKXl70ZUvJJRiU57ApjdeZtMLUwRrFzgm\nk+SNQReCE47NT7Np/r/BbmwkcS1MMrGBE0MvEjGSXNf+Pjq7H2VkdH9JPvgXtc1z6uQ3mVuflJKB\nwXSpKc+JjkFSw1moBye6judfLpAbrqdh1UbMSA3KTG7+CqaEROLJSofTs0EIUSOlHJrss0ogcIFi\nUzjEk3aWbEGnCYMB6dBnWtwYjfFaocBKOzzuiX2lDHEom5u1QKBJNzgZy3IiK1kmDUbxOG4UuCE2\ns06EGxMGrw5nWe2G0REcFQUaEyqGUDiedU9xIgBdKLRZIQ5bFmtD0ydJnsn1TkoJAt4RT/KOeJKv\njw7x1ZFB7k718pZoFRvNMCs0vVRC2DbPlrhLAWeS5I2VDT452EW1ovKnNY0MeZK/G9mLIlSuXPk2\njHwnvxNyuTy6qiTX++RgFx7MiZPfWBBw/8AtPPDwCxzc9QgnX32OVNcxfM9FKArxumZa1m1l1RVv\nJlHfQqS6FuvgEaqbZv1wKpgUFY7ANPAT4PLJPqgEAvOIrOexr1BAAJvC4Tnt1S6E4MbqGF22Taed\no17X2GIGhLq067NMTJJKc09/aybYFo8yFHY4UsiT0FTeaJ77k/lENOoGtXU6+/J5HAlXhkOnyHST\nlAmrhUaHa5/+wRSY6Hq3PRRlyPfYW8jxmmWRlT4xVeFX40lqVI1boglUAf88PMBrdoGP1jQuCTLp\nQmMqSd4lRoj7CL7KHaEI/zjcT4froCO4I1pHY++j3BzRaNZ1IJAP/mlNI58YCBrhXD2LLa8t6ZO8\nNsmjb/0w//xne9nz+FcYPPkCTiFwDUSohKI1FEZTZFP99Bx8mQPP/py1295PvHbNrB1HBWeDKGIw\nEAAAIABJREFUrJAFp4cp62aVQGCecLBQoHvEZ7Uf1JKfy+RZVqWxeo7lbC2GQcuEp/xqXWVUeiQm\nBANiDu6GGk2nJja7KVFNCLZETh/whcppwUBKutTq068fPpXPsN8u0KJqbA9F6XQdvjQ0gOooNAmD\nZnTymocf9UGFZk3nXfEaHsiMsN+x5ky+diFiMkle+Tf1g1zQ7XONbnCLqOZGuwrVhiMFi6G4O6n6\n5GwMganc+gBGslbg1tcXOPb1j+TJrbyOJ7/4U06+eoKeQ4+D9DBjDbSuexMNq1+HbsZwrAx9R56k\n6+CPyQweZvcjf83mnR8j2Xzp+V2YCs4JAipkwSKEEH81xUcSmJLkUgkE5gG+lHSMemyQp1j062SE\n19I5VpnGvNeJ202TR80M7VYEUyhIKTmsFFgbnf8a5mz2ol8V0ziSyrPKDyGBXX6GnykjpFP+lNv1\npOSEa5dseh/MjACwIxx87vo+27w469Qw0WLgJH3Ja9k8ddVBgDUd+dpsnueFhImSvMdyaVJFTXhI\nCH4jXsMKx2CzFy/9dlbLEAfSOfIhj5esPHcP9TIWAz5fyHH9FD0UxlL8Dwzfiu/7jA4N0N/VSX9X\nBwNdnWTTo0Admm5Qt6qF+pY2enskvYe+RdeBHyEQmLFGNCNKPtPLaP8Balq3opsxWjfeTsuGW0l1\n7ebAs/ey5/F7uOyNfzWtzICUPkOdL5Hq3k1m8DBS+gihEKtdQ7L5Mmpat16URkHThQC0C9xG+RyQ\nPsNnU0ZLQi7SftkzxQYzJL/YtHKhDwOAHsemZwAaxfiJtkvarGpQS01i5hOulLySy1NwJCiwPmJQ\nM8/tcadk5c/AwGnAdXhsJMN9+RQdvjNuu52OXeIZrtIM/ry2iXVGiH8fDfgzDarG10YH6XYdvt26\nmjpV57l0lmXZIPvgSskLMsOVIsYxPc8Ndafq/6Oex1s7D7PGMPlC04o5P88LFb/dc5zDjsVf1jbz\nNwMBWbAWjf+iJrlBxHmNPOsJ85g/wk/ECH2ei4EIlh/snvT6A2x4YyMnPnY3//Nrr7DnsZewcoP4\nXnA3qHqoyOavIxStRQ9VIYRCevAwB3d9kWzqaLARoRJJNJFP9yGL91a0ZhXtV39w3IQ/1Pkiex6/\nm1jtGra+6W/OOImnBw9z6LkvkRk6EuxC0QnHG8btI1a7ZkmWG5782rvGvRZCvCClnMxafka4/Mot\n8qfPPHDe6yfN1XNyXIsNQohfSimvmOyzShg1D4irKkeEDYyfaPOKR0QsDJNYE4LLo7NXTz1XzFUv\n+iOOzeey/eO2u1Y3Sfkexx2bPVaefXaBo65d2u4dsSoSiooqBN9Jp2jVDeqKDG9dETjSRxcKL8gM\n97jdfFxroWbC4J5Q1Unla3N1nhcqxlwHd0biNDZqfCbVyz7b4qteP1+nHw8orwCNBVAf+NJtfON3\nv0VIV7nmH3aSKzhlTXmyPFi9jcf+7Iv0HBpCDyWIVC0jFK09ZdM7IRuT6n6ZPY/fU5qMdTPOFW++\nByOUIN1/mM69P2ak/xWyQ0fZ/aO/YuPr/oTatisBSLZcRijeRGbwMKmul6hpnXTsHbePULyJlvY3\nVMoN5wlRIQtOB1OmHCuBwDwgqqjYIQ8nf0pb70gfP+QTusBc46aD6faiP9emMi8Xcnyiv4OwUHhn\nPMlvV9WhCMGj2VFSvke7YXJ9JEa9orHfzvN/h3pL202awU9houvdJZEwT+SybPaiXClifFxrYYUw\nCEfGT/ijnneafG2uzvNCRvn1D8oGK/i31CAncg5HpUVG+sRQqNU13l5dzY5QlGv/ZScvrHgvB7u/\nxLIVbdz9WhtDg0NAFaqq0rSpiX3HwniuRdums9v0pgcPs+fxe9CMKPGaVQx1vciyzW/FCCXwfZf8\nQIrlq34DufKdjKb2cOzAv7Pvyb/lsjf9NfHaNQih0Nx+C0d/+TWGunZPGgiU72Pdjg+RbLlsXOZg\npuWGiw0VsmAAIcQowYQvCazb8pyizkxJXrr4ZqEFwvVVUTqjeQ6qOQ6qObpiBa6ruvie/CbrRT9V\nbby8qYyN5DOpPvxJSlmH7AL3p1P85WA3DkF9v0nTS88IN0XivCOe5HWROOuNEDWaxrWR+KTb3WiE\nsJE8nA24AroQXJE0ORTKcVTLkzQVzGqf9RN6NjyUHcFGluRrc3GeFwMmXn9FCN6frOXN1Qk+EK7n\nj8ON/Failo/WNZBQVDrfs5z/+XIDb7zld3Ech/5hjX/7Ti8/ei7B43tW87ODW/n/n2pl6GQNkUTz\nWYMAKX0OPfclpO+wbseHcKxRFFWnYfXrAEj3HyUSWjG2ML7vsqL93Ujpcej5LyNlcNc1rr4Roehk\nBg+fdR81rZdPWT4QQqGm9XLW7fgQ0nfG7aOCAAKJwDvvfxcSpJQJKWVcSpkAXh77u/j6panWq2QE\n5gmaEFyTuPgm/omYrBc9wOFCgc6sF8iBtcDpsLmodtgeitKqauyzC3w7naJJ0+l1Hd4QTZBUNQSC\nk67DgOfSpGp8oqYJU1HIeh7PpPNIR4CAWEhweTRcSgNP1qxmMte7Gk3nhuqpSzi+lHw3E0jKthfT\n+pOdZ4dtcyTjBHltBZZHtXFNkOajec5ixzuva+O+Hw3zkMzwN2/ahCIEtucTz1qlxjydWYsXPR9j\nQz3PqTfy9MOD5EYD+eCyzXeUUvTng6HOl8gMHSEcbyLZchnHX/42oVgDuVQ39miafLqXRHQTekhj\neOhljuz7PKs3fgjdrB5XCtDNGOF4w6ST9sR9jGGk5yD2aBqkQOiSREs7RiggPk633HBxQqAuUIl1\nsUMIoUkpx4ThU16kSiBQwbxiYi96gGMFi+FhQftYEwIP9llZjDpBrabT77ks1006PZdHs6PcFE3Q\nVEZsXGOYuJngCfod8SSmEighnh7Osck+lY5Ppz1eIM9VRVOjydj+Z3K9m/KcClk6XIeNRogdxUl/\n4nkOuQ7HUmWGRx50DFtoNafcHOerec5ixTX/spOBnf+DLdfeziu7X+WupqtoaKynr7cf3/cRQlDf\nUM/GZS20LWulQ13FT+95DlVNY+cGidWuoaZ1Ur+UaSPVvRuA5vY3IISCEAr5dC/OsEc0soaIsZLR\nof0IRae65lLWbPwwieRmMk0H6Dv+eKkU4FgZ8uk+YsnlZ90HBEGAzJhEQw1AsSvh0Vdp2LC9dBxn\nKzdcvJD4WAt9EIsRTwLfEUI8DNwA7J1qwUogUMG8YmIveoDOvEurDHFCWjztp8lIj8MUSPe4VKka\ngqAxzxhJ7DfiydPIXRO3e9S2aLFDgQSwjO3flZfjKmWT9bifyvVuInwpS7V9A8FHkg2l5fZaeTTg\nqGPz2z3HGfU8wr7CGhHiUhHBR3KVEud4Lk+Lrpekha9aeQTwg+wIEmZNWrhY5YtSSkZ8j8Y/vYyv\nZbbxxQ//A15yLULZy2fu/mc23/RWVm69nnhdC7GaBvKGyYkssB9efOjZUv1cKDprt71/xjK7zODh\ncaWAWO0a0oOHSaf2EA41MDz0MqFwE5nRQxjhaoxILTn3KCuveAf9J39eKgX0HnkC6TvEivX8colg\n75EngmWO/ox8ppdk82V4WUksvBbpewwPvUx1zaVEzNWM9h2mqrEdCMoNR1/8JsPdL3Po+S+fk9Tw\nwpYoSpQKWXAyfAz4ILAF+AXwuakWrAQCFcwrPClpLsolBzyXOlXD8yUP+in2eDleIV9aVpUQVtVx\nMrsTrs1+u3AamW5ij/th16eB4Em7nO1f5Z+d7T+V693tZWz/h7IjfDczTIfrYCC4q761dEz7rDxH\nXRsXeDA7goEgqagclzaHpMWPCOrf/xWXpKPwod6BcdJCDYElJfdlhqdsbHQuON/mSTPFZE16PF/S\nk7PoyFicLKb6vbVJXju0muf+7TGMSC2h6OWs29HKa898npd++B/s//lPaW6/hcbVN5YY9b1HnqD7\n4KMU0j0IRWfzzo/NColOSp9QrAHdDKLFZPNldB94hP7uJ9GNGo7uv5c1Gz9MVd2lZL391C6/DE0P\nsjxjpQApfboPPgpATctlp0kEQSAUjdxIB9nUUboPPEIo0sTKde/DtlIc2ff5YB+1l+E4p550C5le\nFEWQT/eQL573mNQwPXiY7gOPTCo1PJNE8UzrLSVUVAOnIIS4nCADAPAzKeXnz7ZOJRCoYF4w5Ll0\nuw5Z6dPjOnxtZJDlusmbY1UYhiCcV9hbDAIa0dipVHFNMsRV0Rijnsf96RRfGB2kIOWkMruJbP91\nIYPdmQKrZbjE9r9CRDmq58cd12Rsf5jc9e7e4YGz6v/L5YIAv11Vx9ti1Ry3LPxhledklh95w/Tg\n8B/eADIf0CLGpIXXh2O8t/sY7brJm6KJGUsLF0K+eM2/7ERsvw6AQsGmoytVaszT0TWE6wYErZpk\njEvbajla9Ssc//fDtG68opTpSdSvI5xoLj35Hv3l1zj20n/OucY+KAX04VgZdDNGTetWQtEmCtke\nFKEGE3TNFiyrl+pl60tBQHkpINW1m0K6J8gGCJXdj/z1OIlg39GnyI50cuWbP8VQxwt0HfwxhXQP\nr+2+hzUbP3xqH4UeostagDGp4aeQvotQdFZtfde0pIYLJVGUUuL7PoqicPLkSV588cUZb3MqCCFQ\nxOx1Tl3KEEL8d+B9wHeLb31ZCPEVKeVnzrReJRCoYNbhSEm/5zDseWwqTpC/KOQ46djUKCqdOLhS\ncl1xwonogq/6/cRQ+T2tkTZhkAo7XBkJPk+oKmZRZnldKMp+u3CazG5ij/uYqhGL2RxLF1iByVYR\n4YCa45L4+AFjItu/HBNd7/YWU+prDJNNxZT6jrKUerlccJ1u8oyV49l8hp/lM3hSkvV91hLiN9U6\njvkFviVTAPxRdT1vLxITvzk6hI1kixmesbRwIeSLmz99LT9vfAePfLePga4Ohgf6kVKiKArJ+g3U\nt7dS39JGXXML4Wjw1H3/X/0E3TzdDTBeNORJdb3EUNeplHYsuTzgA7RcRrJldlPaY6WAviNP0rrx\ndoRQWHP1+9n7+N0cP/R1VrS/l0KhFxH1MCM1pfXGSgGamSiVKlrWvYG9T3z6NIlgPtNLJnWMoY4X\nShLB/mPPcOi5L3F43+dYd+lHsaw+iLiYkZqS1FBRNDzfpXHNTlo33l7a91RSw/btH+Tgri/Oi0RR\nSh87P4yVG8TKDmBlB3nxxXVceeWVxGIxbHv6/T7OHT6Swhxuf0nhg8A2KWUBQAhxF/A8cMZAoOIs\nWMGsYMB1OexY9HoOg56LL0EIeE+iBlMopDwXXQh2W3k+UUy3f6N5FQAf6j3BfrvAf69qYIVq0GgE\nLXv3FYIf94aQyft6jtPhOtxd34oAPt7fyUYjxOcbl6MIwc/zmXHbHZvwUq7LoYKFrgg2h8d3XPSl\n5N3dR0vbnQk5z5eydB5/VF3PfcWyAQTRdp2qMeh5OMVMgYEYl1X4fONyhlyH3+o9yYDv8n/rWriu\nzCr3mXzmtHOe7vF8qr61RHocch2OFGwSmkq7aY7jWpzrPqSUpHyPvqI9c/htK9gVuo5nHj+KUDTM\nSC1mtJZQtA4jXIOyAA6a54rBjl+y94lPEY43ceVbPl2aOIc6X2Lvk59G+i5mtJ6W9W8qlSrswigv\nPPgxXCtwdxWKzqYbP8Lx3d8mM3SEzTv/tERi9H2Xzr0/5NjurxOKNXLVr3ymtI9T+25m8+v/nHCs\nDil9XvrhX5AZOoIeTuLkU2ze+XGqGi8h3X8YECQa1o67tmPOhopq4nvWuP1L6ZMZOIZr54nWLsMI\nJU5bbzqOiK5TYLTvIIpQidYsp+fQo/h+QE7XjAhmpI4ffOUPWbVqVWmduXIWvPKqTfLpXf9+3uuH\ntKsuGGdBIcTLwDVSymzxdRR4Rkp5xjTP4v9lVrCoIKVk1Pfp9Rx6XYetoQhxRWXAd9lnF6hXNbaY\nYRpVnQZNwywOJsniQDWRle9DSWb3q4mAYX+0YPHMQJ7VXhiQfGUkdRorf6LMbiq2f1LT2Bab/Daf\njO1/vhiTC9YpKp8bHsBGohE0dPyw0sS1SoxXtBwn9TwP50cZ9IL0eK2iss8u8NXUIBSC67hSmKRz\nkpzpEVEDzsO5Sgsnky8+l84isyrLZJhRPB4xMtxQHZlyHx/9tzeP26br+XQOZktNeTr6MxSc4Dxi\n69rprrmF/T/oprn95pJN71JDTetWYjWryQwdIdW1uzSB1rRu5bI3fnLyUsVoD1IWr0PNatZe/QHs\n/MhpEsFCeoDRjiNUR7YRiT1FLnOCrn2P0Lrp1tI+QvEm8ulu8iMnCMfqSlJDI5zEzqeI1a5BD9Uy\nePAVIuGVgGTgwEvEmluJVAecjGTLZRjhauz8MEa4urR/x8owdOQVwsYKDK2G0WMnUOMKydZNpfWm\nkih6ToFCbgArO0Bm8DiZ/uPEYu3UNu5g+OghND1OonEdZrS2VC4pDwLmFhKlYig0hi8Azwoh7i++\n/tXie2dEJRCoYFoY8Tx+UcjS67nk/eBHF1IEq32TuKKyVjdZq5toZ3mKnMjKX19MyY/J7HwpOZZ2\n2eAHzPqXZI7vu8NowJ9Un2LlT5TZzRbb/3wxJhdM+R7Visqf1jRyeNTmy/YgX/P7iSsKW/0oEV+Q\nDXncnx0lIhSGiw12nsnm6JQ2OoLfURtZ7Zi8mMlxXVWsdN3ORVo4Ub7YZdtoGY1mYYCAKjQusaO8\nmMmXjK3K93F8ezUdOz7Ggy/009/VQX9XJ0N9PfheoMqoqqmlrr2N+pYg1R9NVPH9Tz5Gom7tjK7j\nQkMIhbVXf4Ddj/w1B569d1xKfWKpIj14GNfKFNdUWHXFe2jdcCtCKBx6/svAeIlguvsYsWjQ+XD5\n2nfz2u57OP7KdzDjddS2XTGpRDDVFXjAuE4eoeisueq/UejvJxZtLx1zLNpOpvcA4aomhBAIoWDG\nGrDzw4RijaX9D3e8RiyysZQFikSWkU93YuWHMcPV4/Y/cPJ5QrFGIlWtAPQffwYrN4hQVKz0CDV1\n2zFDdQihEI2uIpPzCMebFyzrUyELBpBS/qMQ4klOkQXfI6Xcfbb1KoFABeNgS59+16XXc+l1HVbp\nJhvMELoQDHoerZpOk6bTqGpUKWppUDlbAFCOclb+04VgwspLn1HPI+d7GI7C92WKR4qkOh3BB9WG\nYg/6ABNlfxO3ez5s/5lgb1H25wF31jaxUjOISpNPaCZ3uZ38H7eLRjR2eHFeVQOp4x9V13NXqheA\nPTKPjuBjajODOKzExHPGX9PJznkqTJRTdlgOy8TpzZOKfXeQUpKWPhuMEJqq8K0XR/n+zjuBYHI0\nwknMaC1mtAEzUkvXkAmHAHqL/y4cxGvXsHnnx9jz+D3sefxuQvGmcaqFeN06cqPdpLpfppDpRSg6\nl9w0nmQ3UYYofQ/pqqW/bSvFmk2/w+G9n2ffk58u7aO27SqOvfSfjPYfpGPfD+g5/DgAvudyyU0f\nRzcT+MVOe+VSQ1XGcO1MiWsh3aAm77lltXlHIHQxbr1QuIXsYAeiTqGQ7kHVwyAUUl27CUXrWbb5\nDoSiUt10CUJR0YwYQwf3Eo2Of9oPGS1kUyeI162ek+/kTBAoCHE6x2dW9yHErcDfE7S6+Fcp5V0T\nPhfFz28HcsD7pJS/nM66s3ycK4Bh4Pvl70kpj59pvSkDASHEQ8DvSSmPzdZBVrD44EqJVnwS/352\nhEHPRRbr+7WqhlKciyKKwq8nkrO23zFW/h/0ncSSkn8dGeSrI0M0aTpdRendGN6p1rJKMwmXpZqn\navJzvmz/mWLU95BQSsVnfJ8hmcf14Waq+BEj9OLyPT8FPugIPp06NYFqwN9oyxjEKUkdk9NsbDQZ\nyuWU1/zLTtI/PYp9fz+6UHg6k+Kenm7+uH4FsbUJOm5s4WR/hnTegdZlRL+UZ3S4QPPGzZjROoxI\nEuUia/OabL6Uy974V+etWpgoQ0QIKKavxxwJ12z8MKs2/BY93Q+RGz5R2gdIsqmjHC12PVRUnUvf\nEBD4HCtDzh88bTt6uBpFLVO+iECiOC4+L95OQwO/5NhrX6R5+Vuob34dqmmSH+1iuOdVNDOGEa5G\n1UI0t98CxXswFKsvnZecxJbXc3OEQpO3f557+ARz79xACKECnwXeAHQAzwshHpBSlhv03Aa0F/9t\nB/4F2D7NdWcT3+dUr4EosIIgZN9wppXO9Ov+MvCIEOIrwN1SSucMy1awBCClZNj36C2Su3pdh7ii\nclusCkUI6lWNZZpBo6ZRr2oYc1zj3WiGWaUZHHIsbotWccix8KSkSdHZTIRLRRSJ5FIRpcPMj2vQ\nNJXsb2y758L2nw3ki5PzbdHgWiZUlS4cbpBVbCLCRiKA5FFlhEOygCUl7bqJJmCvbWGisFYJsVKa\nfFxrYZUw0MLj051nOueJGJNTrr97B0Ov/wvWXTXKF3/xMWKdKdJmnBuTrRxVNWLL1hNKXkrrpS1c\ntqyVA7kkI/c8SLxmBVWNG2ft+ixFzES1MFGGKISCGtHwPXucI2HWPswVt/+fU+WG/gNkUsdRNJPG\n1Tcy3P0KhewAoVgjEDD9PTWLlH5pO/HqjVicHN9HQUqkHwT1EPADClYn6YHD+G6BZN2VIH3Smb20\nrr4F33eIJVfi+y5Hf/kNzGQSvYxIWH5eY+ehqEZxVz62HCAZWygPAjnXpYGrgUNSyiMAQohvAncw\n3qnvDuCrMmDfPyuEqBZCNAMrp7HurGEiKVAIsQ34g7OtN2UgIKX8dtGa8C+AXwgh/h1OXe2z6RIX\nGseKjm4L7Zy2kHCLE39dsW73WC7NMSdIFYYVhUZVo7Vs8FgIS9tNZpj9jsVK3eATtU1AID98bjSL\nYwkE0B0qcG18PJnvTLI/COrd14Zj839OxZF3yHNZJ8LYio/jSRx8tipRWjSDp/QRvp8d4U3RBHnf\nZ69toSlwyMwhHahWFETYZ1NkfLbibOdcjnbdZJ9d4D3f9Tnyt5/Ezg/jF3zCqRGSjotmNnOy5RJC\n8gZOvhyBlwHSdOz7Jki35Ih3sSNo+nPFOVv6TpQhAtQs30Lq5Kt4eQctEqcgj1O7+tJx++jY9wMy\nqWM0rr6Rtdvez6Hnv0z3gUfGbadu9VaGTryCtCR6JIatdFG7fCsQBPtOYQS/1ONAFt/3kcJDhHx0\nP0G4qg0jEiPR1o5QFFQlCC679/1gnCPiZCg/DykFiiGpXb2w7ZHnOBBoBU6Wve4geOo/2zKt01x3\nziClfF4Icdab92z5PhvIAiYQh6XDyJDAYdualnOaLyXPZ3JYRROvcAiujEYWTeCwP1+gL+chfBC6\n5LJ4iPgkpBxL+mVP+y4DnouPLEn42o0Qy3WDRlUnriin2fQuBCZr8qMLUSLJTYbJmvwsNCKKQsr3\neDg3ynuravGkREMQU1V2iTSfdXv5uNJCk9S5IhTh+9kR7kunGMvdxhSVG5JTn4svJQ/5ATHtndcv\nY8vy2tJnUkpSllty6uvIFIhctRHue5bH7rufVZe/m0T9ekJFOd/Yk9zEKzzREa+C88eYI2H3wR/T\nUiQQCqFQs3zqCXOy6z+2nc79D2NojSAVhArxplWY0SRSSoQI6v59R3+GlRvEc20KmaDsZOUGkNJH\nN+Ms2/wrZ1RyTPf7P9t5zD8kYmYdGeuEEL8oe32vlPLeGR7UgkEI0USQxYDAQ+B2IYSQZ/AKOBNH\n4FYCE4IHgCuklHNXhJkDrNIN/rZ+2bSc0342kmV5PlySullpn6e9LNefYTKaL+zPF3BGFNoJInbp\nSp52stxSGyWHpNd1aNEMIorCYdvimXwWRQS69U1GiEZNQyWYbJbri899a7aa/Cw0EkpQCuhwndJ5\nvKxnaHaNcc6Gh40cN0fi/Gc6VbL8FcX1z4SRD67hyCcPsO3y1dzx5T+ht380cOvrGORk5yDZnAWN\nEAmbLG+rxU3cxgOP/T654ROBhKzpkrOeQ7kjXrJl62xclosWU8kQz4TJrn9N61YiVcvIjZzEzeaJ\nV63HtoboevUnGNVxzHA1dcu3I4r3T6SqDSs7gO9aCMXAtTJl+z9z4L90v38FmBHXZ+AsPgKdwLKy\n123F96azjD6NdWcNQoh3AncRNBySwD8Cd0op/+NM652pCPw/gHdIKe9cakHAGMac077RvIpP1bcG\nDmv9neyzTtnM5nwf1VJLQQCAKRRkXsHyFz4B0pfzqCt2j7SkzyFZIGV7/GOqn2+Npngil6G7aFyz\nUje5PVbFexO1vCVWzdXhKCumIelbSIzJ/gwEdw328Ew+gz9F4OpLyTP5zKzK/mYLG40QkoAWPHYe\nmxMGe7UsQzgsFwYH9ByXVgUB3RvLzIJkcf3J4EtJ32+u4D3/sCuQZq14PTf+0fO8/a/28JEv9PC5\npyI8MbKN1xJvp2v1H9DR/jGeiXyAh7+fYd2ODyEUnQPP3stQ54tT9rEPGtK8OKvNey52jMkQZ3L9\npZR4rkXD/2PvveMjuev7/+dnZrYXabWrLp1O0vV+ts8V8NnGxnEIEAjEkACBBEIg/EgCJIZvSCB8\nQ4sh5UuLQ0nAIZTgGLCNG+FsbNyu+3RNJ516X7XtZebz+2NWe1r1Lp1un4/HPU6a3c/s5zOzmnnP\nu7zeGw4ihEbz2W/R0vhdgn0voicTxEf7sdgvNe8q3ngjQii0nXoQoVjYfN3iPv/ywQAZWfi/2XkJ\n2CyEqBVCWIG7MR+Qx/NT4B3C5HpgRErZPcexS8n/Aa6WUr5TSvkHwH7gY7MNWrfKguVWp/xmaSUF\n4560plJO60mn6OmXlE7oZ90lk9SWqBStUl1sWkr69TSPD4TYJV1UCCsRqfOwPoRDqAiHzk0uN6Wa\nBZ+irpkb4kKZqIk/l7K/xWriLyVjyobFqsawniaFWUHwench19gcFCgqdkXNWcdYN0UwhYXe6i3i\nLlcBqjDDWo9ERnjBLukPhgCFktob8RZvM8v4nH5srgCaZeYnoYla83Np3rMUWvN5TOb1ETl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LmDs786BhwDIXBGRnArVozSLTiqriYZVdEsUH+Na15x5ql6yiuqheg1b+ZX4QGM2BCWotqcrPG5\n9HSX0uDCi9/KiZFLKUn2nMYT7CBuc2JsvAZVsy9LjHw5mGpNAKlkhFBvM9KQ2NxeXEU1gMThKaN2\n/+/RcOgLNB3+Dm8wykFz5IRc/HhJp6JYVdP4HQu56OmE2TI3OmBq80uJZnXhLKhECEFgw/VoFmeO\nTG+emRFCIVB3NcnYCMnoEEUFW1G1heVCjZ3zL39FsG9fHTfdeM0yP3TJOYXhrhD+FLPx0ZhlJIFD\nwC2YzZCmZN0aAgWqyoE10qt+KVCFoCuZZFTX8WZCF0IDmZA5/e6jUsdlufRHtxwCSjB1zkJzPEH3\niI7DUGiVSToHdfrcEQ54XKBJ9qVdlKLRSxoB9JGk3Hqp3Gr8XHffWY7nM+8GIBZL0t4ZpL1rkLaO\nIJ3dQ5w+18m5C91YbB78VVdhdxWjGgbbTjzB/gQoIsXowEscGuknvec3gfnHmWfqKW9xB8A92W06\nl57ug53HCQ824/CU4avYi5QS24mfcGdXJy6hoUuDIx1naDnwRrRMvHqhMfKVYuKaAOLhAUbbW3A5\n6xFCITU8SnDkaDbk4vbXY/eUER1u45miQl6HIyfk8pLLdFOHBy+SjA3h9m8mFurD7vITCjZhdRbh\nLd6K3Zkb17e7Lgd39trE6ijA6iiY/Y3TEA8NMNrRistZx3MvKjz1qxO8+OIZ/uLP37GEs5yCvCEw\nRqeUsmX8BiHEgJRycJr3A+vYEFhNlkNJb7vVzplknJ9HRvhdbxEAe112nk1E2JJ2cq3iJiR1Wm0x\nbhsXX1+sgNJ0TJWz0BZOs1U6qRd2LsgEDUacHVE7YWea7R4bJ1NR7tAL+a4xwGEZocihsNc6ea7X\n1vk49kcf54kH+ujv6mAkmEnUUxSKSnYQ2FLJkafM0tjqnW8gUG1m63uOPMDVSZHNfPIKjes7W/jl\nhnYshdXzLu0b6yl/4vFPcf75+6Z0d49hNnU5Maee7kPdJwAo33w7Qigk+y9wW3cHrkwrbFUoXBsz\nCDY+S2zf67JzWYmyxIVWMExcE0C4pxW3a3P2PRaLFyOexO4pJxRsov/iM9k1nbVp7E6l2airVChu\nHkv3cd5WgTz/BACKaiU81Io0UhSU7qR65+sRytrL5bnSCfe25pQSWm0FPP/iAGfPNS7fh0oJxpVt\nCAghCqSUI1LK2yZsdwH3zzY+bwgsMculpHedw8UD4WH+JzzMmz0+FCFwqSq3BtycisZIpCUeq8Jt\ndnf2xrwQAaU5r3NCzoIuJSJtfu4e4eQxRnhMH+Y2sYGWZIpdDgc3BVS0sEQbhlYlzisLyjGkZMjQ\n6U4l+d6oabSG9lzHX372R/Q1j5pZ3M4ANlcAq9NHx4AGpw16zl1AUS2U1L0qO6fCzFrHx5lLhIZl\noAUKTW2/+Zb2efz17Dz4ERoO3UvDoS/Muaf7THH8cLApZ+6OwXYKsUyauy88TGzcuPnOfb4spoJh\n4poApG4aBNLQGR48SWHRHmz2AA6X2ZSm6/zjbL3pgyBUhkbb+dVNf8qJYAeRxAj9ioGzcAN2VwBr\npozvyEMfAcBfdVXeCFijjJ3z8VisAY4cXkbJYaGC4pn9feubF4GtY78IIa4F/hC4lZkrCYC8IbCk\nLKeS3g12F9usds4m47wQj2Rj8poQ7JumIdFcBZQWwsScBQUwFAkGGBlFyx5SvOSM8pHP30JVqfmH\n+oq0zrf/5IcIBE1vrqBjIEIipdPYOcLgUzob62pQt78e8XKcqp3eaZ+qpTSwu0uy2fwAUbsToiM5\nceY6i4+U289YAGI+pX1j+Mr3sPeOv832dL949H5ajv9gwT3dJ8494fKRkGlsQsuZe9SeW9e9kLnP\nlcVWMEx1PlDM78Hw4Emaz/wrddv+GE/hZrzFm7E5A8TDvXSdfQSL1YmUBqq9AGPbFuxSsmGCx2yw\n89isIZc8awBlcn5PKhWiZuPG5ftMqYM+snz7vzw4JoT4GfAM8BagHbMB0fullPqMI8kbAkvGcivp\nKULwYV+JaUAEe6b8jDFmElBaKibmLAgh8DkFQ+E01ygefp80P9CD/Ht6hFTnfkqoJtjdRXfbRZp7\nIhQWF/Os5zUEtlTQ39XFI4/di2axcuDtn6Th0OlZXdNTlfb11+yjY/iX2TjzNq2IQz4vWun27Lzn\nWto3EY+/nn2v+btJPd3dvg3zbiQ0ce5qxR6ebTnOwdF4du4uu4+zNfsZ/9y70LnPxlJUMExck5QS\nzWVnuPcYSAN/6Y0IRSWWaqV447WUbjpI28v/w2D3SfRkFLe/BIenNLOvS9/V+YRc8qw+dp+fxGAf\nNrupKyClQVVFiJtuXOYuv1e4joCU8m4hxC2YFQMB4Ang7FyMAMgbAkvCbKp/UsrsxW0xSnrbbQ4+\nV1zJPf2dfLS/kyrNwhvchdw1zuswlYDSckj2Tp2z4OCIiNAQTVG03cdB91aefOY5vv6Jj6JZ3RSW\n7UJRrRiGjubcwhM/PUV34z9lXeq1V72Nn/+/T8/JNT1VaZ9Sto1fa1aKWk9gT/l4uKCY1OZX5lSJ\nzKW0bzrMDP6rFh2fnzh3oSgEr/1dHrrwDEWjQWLWcoY27EMLbMwZt5i5T8d02f6XXr/03Z2pgsFV\nVJuzpu7zj5NKhEgmRzGSKeyeANgSBDa9CkXVqNn9JryBzZz65ecBg9hoNx1nHl5UyCXP6uMJ1BBR\n2okONVNVoVC7sYA/eOcfL3vVQD5ZEKSUvwR+KYQoAN4KfEcIkQa+LaX8xkxj84bAEjCd6t+zaY3D\nlg2ENDdF6SFele5gl2ZargtV0rvW4eIrpdV8aaiPM8k4Xx7u577hATMPIZUimXHLL3VHv913luf8\n/rutFh54bJif6iF+a18VnZEEHeE4kZRGwbYy5PY7EN0u6hPb6Tr3ONHhNgbans+O7295lr6LTwPm\njbGk9hVcPPq9Obumq3e9HiCntA9AC9QxGqjL1s6Mf6KeS2nfSjBVWaJqdZDecTt9mfdM/MNcrrlP\nle0PMNJznuRwCGmY1SmOQCnuoipz/hV7sblLCQebaD/1IFanDz1l5sSMrcnjr0cominYNI1Mb2HZ\nLqwOH8loED0VXXTIJc/awFVUjauoms9/9u6V+9C8IZBFSjmC2Xnw65lOhO+ebUzeEFgCpsqgP5EW\nPFV4EM3mxwqEgZ+FmimNvUCxujjVv+02B/9auoHnx7T9M5UJ9VYbOzKVCdcvoDJhOnbfWU7yn+8n\nkUjQ1dlNZ3snva0dlJ38B1q7e/i6o4arXrmP6upKqqorqayu5CtPJYmdvUD5pnrK6m9hqOs43Y2/\nYLDzKIpqxVlYjSfjUtdsHk4+8el5uabbXn4Qh7eS2GjnpNK+6ZhLad9KMFNZ4nQs19ynyvYPDbQg\nwzZcTtNVL6Uk3NVIKjlCYemOrChPX7iXYOdRyjfdSkntTUSGWoiOdMxrTcloEHdRHRt2v5Gh7pOL\nCrnkuVJRQVl4yeN6RkrZAHx4tvflDYElYCrVvxNqMZrNjyF1ekcaKS3YjOKu5bloA6/L6DosRvVP\nEYIbHe5l6x8AEDUMyj6+j0cqfov73v8VIiNBMxYnBK4CP5tvext93/8Xfv7zp0hVH6SndC9HLioc\n/+rpKfYmCAWbEYqFPbf/TfbpTkqD449+YkENaBBm+GApS/tWguUqS1wIU2X7J0aGsCnlhEeaGAoe\nx2LxoOsJ9KER3IXVWOxeqnb8Fn0XnwUpKdt0KwAWm2dha7r23Xj89firrl6ydeW5gpBpSA+t9iwu\na/KGwBIwlepfWpiHtnekkZcu/pADtW+hvHAbKXHpkC+X6t9MTHTxjyGlJBhP0R42Xfwd4QTR2gJi\nnVX86pfPMdghsTlLMuV8ftJJMw9/x6s+SsOhe/nZvX+2oNK6qVzTUkpk62H8/a0ADJTWoVTvn9SA\nJjbSSc2e36Ht1E+WrLRvpViOssSFIKWBzVWMnooRHW7HUVAJUpBMDdPb9b8MDxyhtPJ2fMVXYVij\naJnETFdBJU5vKXDpu7tW1pTnCiQfGlgUeUNgCZhK9a861UevkaK0YDMHat9CacFmkokhdhjDoJpP\nScul+jcd49X6dN2gq2eIto4gbR1B2jsHicZMuV+X00ZNlZ+CTa/hRy9b0JqHKK2b+il0saV1U7mm\ntTNPckdLI45MA5pwfx9PxkYxth4EcsV1kvHRJS3tW0mWuixxPujpBKP950glwiSiQbrOP4GqWVEt\ndhS7hpYsZEP93fiLD1BQtJtUahRLIJA9R9NVMKzmmvJcwVzhVQOLJW8ILAFTZdDfpqXo6X+SVs8+\nSr2b0cNt7I+eZLvl0g11uVT/JmJISe87avjHJje/2P0J9GQCQxq4CgMUlFZTVr8Lb3E9Hn8Zbn8Z\ndncBHUJw/BumGphQZnZFL6a0bqJrWk/H2d5pGgE5DWg6znGu/kaGe04x1H2C0X5zbr3NZmfNDbve\nAIgVjTMvVIVvqY7d3OYoSScjmU58A1jshXgDmxCKSjjYjN1dQiLSjyIUqna8DlWzIgsN+psPYzEK\n8RbtIpboQnFIXL7d2f3OVMGw3GvKkycHoYJWuNqzuKxZNUNACKEChzG1kV8rhCgCfgBsBFqAt0gp\nhzLv/RimSpIO/H9SysdWZdLTMJXqnyIEb9dG6Qn9ktYRha2qQaF2KXSwnKp/AGFDpzed5nA8wk8s\nMdr+/rz5glCw2r0k4yEig330NZ+m+/yFzNOZCvRn/s2PhZbWTRSiScWGKUukQFVzxHXSaYUTj/0N\nkeHWzDIsCEXDSCfNDPxxpYWbDrxr3vOfL4tR4ZvIUpUlQm65X7D9MLFQN3ra9PQoqhV3ppGMomhU\n7XwdzsINjPQ2MND+AjX73pJZi0LJpmvNVr6hAQrKa7CM6644lwqGpVxTnjwzItOQmlFKP88srKZH\n4EPAGS61S7wH+IWU8nNCiHsyv/+VEGIHcDewE6gAnhRCbJmrUMJKMJ3qH0CZplIG5BayLU71b7yL\nH8AwDPoHQqabv9N09Y+MRmlq6eP7DzyPrhu4fMUUVdxE+dbXYHMUrpl+9xOFaKzOAC1OB76EzIrr\nqAj+PdSAgZEtLSyqupojD30Ut6+GktpXrug6FqvCt5QYeopEdDD7xC8NPZu8hxDYPWXYMk15LDZv\nThmfebOevoLB7vJjH9fMJ7v+NVJ9kScPYKap5EMDi0LIVTiAQogq4D+Avwf+IuMROAcclFJ2CyHK\ngUNSyq0ZbwBSys9mxj4GfFJK+dxMn7HNZpffLNu4rOsYz5lEjA/0tk+rLDiGISXPxcL838EeIobB\nBs2KVYg5NyW64WsH6X3lPTx0bJD+rk4GujoY6O4imTArDxwuN8UVVcSjEb72iY/iLiigcte75pTB\nnU5GVrzf/YWXvk33+cepu+rtWWEg2XKY68++QKXUaEuP8i+jx1CsTrbc+IHsOjrOPMzFo/dTvuUO\nNh1414qtI5S5uU9X6jjGcs1HT8VRNBtCCIZ6TjHadzb7mtVRiM0VwFe+d14CLqu9pjzrk6fvz9UR\nEEIckVIuucTgNXtq5EsP37Pg8cqG9y/LvC4nVssj8E/AXwLjO0WUSim7Mz/3AKWZnyuB58e9ryOz\nbRJCiPcC7wUoVVd2aXNV/fthaIg+PZ0d15VOzdqUKG4Y9Olpij6wg39p28T33/V5us+b7nuLzWtm\n8rsC2DLNWS60SY4/+hnSqSSVu96V85SXDPUiUwmsvqqsVO9q9rufSlxHbLyGZ3zlODoaOHz+Z+hI\ntt34gew6pDToOvdYZrz5pL0S65hOhU8aBsnhNoTVidVdsmTzkVKSSoQyT/tBEpEB0skIFVvvxGJz\nY3P6KSjZnjn3RSiqZfadTkE+23/xJOMhkrFhHJ4SVG1lkn/zjJHvPrhYVtwQEEK8FuiTUh4RQhyc\n6j1SSimEmLerQkp5H3AfmB6BRU10AcxV9Q+gRFX5swMbec+fvYIij53BUJzvPHmerz58ijPdId4f\n7ODjv7ufgNdO/4j5tJ9MbufFIyNEhnwUb9yMzemf8qIT7Dg2qSRPj4fwHf8pu19wYfcAACAASURB\nVIcGcUg47XbSvuNW1EAtsHr97qdzTVsKKukN9RKKDuSsI5UI0378IRKRfhyuKtIhnehwF87CimVf\nx1SljnpfIzVnnmZ7OEpYwCl/gKH9r0OzuuY9H2noJGPDqFYnmsVBdLSTgVbTBlY1GzZnAI+/HiVj\n5Dq95Ti9U5eDzpd8tv/CkNIgePEYIunAYilgqPcsqseCr3LHak/tykFo+WTBRbIaHoGbgNcJIe4C\n7IBXCHE/0CuEKB8XGhhTW+0EqseNr8psW5NMp/pXqVloTSdxCYW/9pfxF99+LcOv/hsMw+B0Tx8d\n7Z14b+7i92s7ePlkA48+9Dj/9/vH+ej/+XNuetONVFVX8sFPNiIUFd8s1/6pSvK8DY9z22AIIawg\n4PpIGqXhF7S/8t0IRVmxfvcTmSius/m69wAGQ90vZ2+Chp6i6fB/4CvfzUh3M72tv0QIjZrNv4/L\ntZFw93kc3rJlX8fE42roaepP/ZKrEhIUKx6gLDjCzxueJL7/9dn1TTcfaejEw33EM0/7yeggUhr4\nKvbiDWzG7grgr7o6I9PrXma99ny2/0IY7jqLTVSjOk2DXLO4SUQHiI704CwoW+XZXSHkkwUXzYob\nAlLKjwEfA8h4BD4ipfx9IcQ/AO8EPpf5/yeZIT8FvieE+BJmsuBmzN7La5aJqn+GlLy3tw0D+Li/\njDd+7mYetr6aL//ZN2h8/gzSMEMFmtWJzRnA5rqG6r2VnPv1V/jyV/6HZy7sR4jmOfdgn1iSJ6VB\n+WAvYkJJ3q5wiqaBRmwlZhvr5e53Px1jrulTv/wCp5/6h0mvJ2LD2coAE5VNO99HMjGE01WN3VpJ\nZKgNt38jsHzrmHhcUz0N7I6nYcJxLR/q4eK4cWPzCfWfJzLUhlAtOL3lSGnQ1/prwIzve/z1ppvf\nFTBXqdlxF9Uu6RpmI5/tPz/0WBLbBB0Qmy1AbKg1bwisFJJ8aGCRrCUdgc8BPxRC/CHQitlTGSll\ngxDih8BpIA18YC1VDMD0an0A4VSa75/v4Wx7nGKHhfgd5dx3sYyf/+cP6b84gkX1YrG7cZfUUFhx\nqV2uu6iW1pd/vCAX9+Te8AI9owUwviSvxlIIqjU7bjn73c8FIcSk5F+haJO3CYiEWuhue5j67e/D\n5a1FKJe+ysu1jknHVbWiI7GQe1x1R2l2THjwIvFwH5rVSSI2zED7izg8ZTi95SiqhbL6W7DYvFl3\nf57LjCmcNFJKmH9kM8+CyecILJZVvfpIKQ8BhzI/B4Hbpnnf32NWGKw5bvjaQQZv/QRgXgAGg0N0\ntHfS2d5JR3snw0PDPHHyfwF4xWvvYu/b38bffvE0qZF6qqovPe0lwn1Ehzpx+sw8yMW4uCeW5Akh\n6AxUsaezPVuSt8Pi51mvE2vRxuy45ep3PxuhYBMNh+5Fs7ozoQHJUPdJepufwkgncftq8BRvwVe+\nh9GeJnqaf0F326NUbnwjBUW7CccbKSm8dtnXManUsXQrR9zPcVMkxTbNx1ucm7GgcNiuMNZ1IjLc\nQTzSTyoRxplJwrPYvdl92pxFSzrHPCuL1e0mFQ5hsVzKe47HunBvqJ5hVJ4lRQLG6hleM2ngjHtP\nNfAdzCR4CdwnpfznzGufBN7DJQGXj0spH1mJuY+RfwxZBNd++VWc2vKHfPqzjxEK9hAK9pDOlPFp\nNjsefxke/y46Bh5G0awEXv0h/vH+XvRYArfLNAKkoTM8eJLCoj1Eh9qyhgAs3MU9sd89QHzXnTzK\nI9T0dWBTCznk8xHc8Wq0cXHnMbU4zebNJo0tRC1vPkyXiT/WgKb7/OOU1L4yuw5f+V4EgvZzDxDs\new5XYCOF1Vty4uczqd4thonHVQiFxrp9NJ5+HFt8iKSAYacdpagSr2EgFIXimhvoPPcoSJ2Csp1Y\nHfmkpvWEt3QTQ6nThEO9KMKGFAnsfn/ewFtJhAaWVT3eU2rgTHhPGviwlPKoEMIDHBFCPCGlHOvQ\n9o9SyntXcM455A2Bcczk4geIp3U6Iwk6wgn6N/t46Egh//vtf6PnwiCa1Y3dFcDmKsfm9KMoHqLD\ngugwxEMp7K5izj7dC4AY508cHjxJ85l/pX77+7AV5LbSXKiLe6qSPEXVSOx9HWf1NFLqqJot5+RL\nadB59ucADHUdM+e5CLW8uTJVJj7ASG8jqjCfsjrOPpKzjpqr30R/57PEw91Y3Co2ly9nHbOp3s2X\nMZleh8eM+bY3PJgVEBKOAiL115GyFWB3F1PhLkXVLoVbhKLQc+EXSzqfPMuPoScZ7jyLkZIIIXEW\nV+LwFE/5Xl/VDqQ0MNLJrMZDnhVEpiExsJozeD1wMPPzf2B6uXMMgUxpfHfm55AQ4gxmGfxUrVpX\nnLwhkOGGrx1EXHdTzraR0Wi2KU9bR5C+gVGkX6IoCp7qa3nox8PYXFup2u5HtUzfL2CiS1lzukiH\nwmgWN4VFe6jf/j48BVtJ24I54xbq4p5JLc6MRU8+7e2nHiQZNT/f7i6lYssdK6KWN1WFw2DbSbS0\nn5Li2xjsPEw03Er7yZ+xYe+4TPwtt08ZNlkK1TspDaQ0UBSNeLifgbYX0NNxpJRYHUUkY4MMdZ2g\npPYmPP76GY2ivArf5YeUBv2Nh3E7tiMyvUEiXe1QpuOYJgFQCGXGa0Ce5WTROQIBIcThcb/flylF\nnyvTaeBMiRBiI7AfeGHc5g8KId6BKbv/4YmhheVm3RoCYcPgS4O9nMmU782k3HfD1w4SvOWv+di3\nTxMa6Mm6+ZPRMACKZsHjr8s25XEXlfCLJ1rwVdTMaS4TXcqe4jqCkaOkY25s9lIc7ipiso3iylxx\nq4W6uOfb777r7KO0nvwRAHVXv52KrXfmvNdic1O5/S4qtt2ZVZZrOHTvkijLTW46lMCISjSnG2no\nFAauIhbppOPsz3AWVeKvugohlElhk0k97g+8a84hDENPm4I9mTK+RHQQX/luPP56NKsLu7skK9pU\nVLmfk0/8Hc1H70ezOuekwjef+SxFI6M8i2O0rwmntS7nODsd1UQGmqY1BPKsIouvGhiYTVlQCPEk\nMNXJ/z85U5lFA0cI4QZ+DPyZlHI0s/lrwKcxV/Jp4IvAu6few/Kwbg2BznSKB8LDWBFTKvd9qLCY\ngGYh8KFd3Ne7k+/8wefpPGMadarFninj24Dd5UezFBAPKcRD0N8SxcwHmTtTueoDtVcTjwwSG+nE\n7gvg8+beUBfr4p6rWlzX+SdIhM2QRc2eN1O5zYzFJ6KDRIKdKKqGt7QeRbUui3rfxEz8ZGwYTTGT\n6YYHT9LV8j+UV/8mPR2PcebpL+asw+EpwdBTdJx5eF6qd3oqjqEnsdi9GHqajtM/zYZfrPYC3L4a\nrHYzlq9ZnQQ2XEpEtNoLlk2FbykbGV0OSGkQ6r9IOhHD5i7E5ata7SkBoCfjWLSSSdulnnf5r0mE\nBtbJPTGWEinlq6f9eCGm08CZ+D4LphHwn1LKB8btu3fce/4NeGjpZj431q0hYLNqfOYTb+Gdd7+C\nIp+bjq5BvvKNJ/nuD5/hTM8wHwh2cvcbr6ditJ4HfnQMm8uPv/pAVqZ3KeN807nq7a4i7K6pk1yW\nwqU8V7U4AJvTT/WuN5if3XkaIyyw26uRus7A+ZN4qmqyMdKlVO+bGDaxOf2EjS6sFGXDJgVFu7E4\nCxnsf47wYHNmHd/P6C8ILh69H5he9S4VHyURDRKPDJCIBEknwzg8pZTUvhJF1Sgs343F5snI9Fqn\nmOXCjut8btprqZHRSpBOxQleOIrTVoddC5AMDtMffJFA/YFVj7Fb7C7So2bobjxi3V4tL3NkCuLz\n75i6hPyUqTVwsgjzS/1N4IyU8ksTXisfF1r4beDU8k53MqvSdGglqN2xS37oC/8v25hnZNCMfwtF\nITQ8xFMP/oh4NMKOgx+joGT7LHtbPKvZ2MXcZ65a3Ji7ORHuZ7DrWLbpTyoZYbi5CZczN+wRSTZR\nvOnSDX9i05+FMlXToeGuM8iIDZvNtPITiX4Uj2FmaGfWMdhxhEQ0iMXuJbDh+qzqHRKSsSFSyTBu\nn7mG3qaniEf6szK9l3ozLC7TeKbjOh8Vviux6c9g6wlsbMhZZzoVAU8Mb8nqrklKSV/jCzgtdaiq\nDSkl0WgznuqN2N35aoC5smJNh3ZUype+9/4Fj1f2//Wi5iWE8AM/BDaQ0cCRUg4KISqAb0gp7xJC\nvAL4FfAyMBbH+LiU8hEhxHeBfZihgRbgj8cZBivCurVxu1o6+cePfwWQqBYn7qJa7K5irE4fiqJR\ns99Hw6Ev0Hz0P1ekyc5qNnaZSS3u+KN/nROjjwy04nSYNdDjSxtlMtdgXCr1vqnCJoUV24kMdRIb\naQHAUVKCs8Cs6CiqvApfxT6Guk8CsOX6P8bl20h0tIve5qezMr0IgbOgEkUxn/gV1bLkMr1LocI3\nXfkkmMmiejKMzV2yJhpELSVGSiKsufPWLC7i0VXN/gZMYauSTdcy2tdIMp5AKJKC2s1Y7Z7ZB68z\n4pEgQijYnD4MPUkiOojV4Vt7jZVWUVBoOg0cKWUXcFfm52eYUn4KpJRvX9YJzoF1awioFidWSwAN\nPxbVTWpoEFWJYHcvvXt7rqzFxi4TY/RWZyHJgVGs1sKc0kaL05UzbqnU+6YLm7h8lbh8k5tMppNR\n+i4+QzzUg6uoFl/FPkIDFxjtP5cr0+v0o2TUBtdyTfdU5ZOGkcZx4iH29bXjSxk0ety0br0Rtcz0\nXK1Wg6glRZnsiZTSAHVtxOGFolBQtnW1p7FqxMODjHY0oSmFICX90RdRLR4ctgrC+llUp0LRht2r\nPc0MeWXBxbJuDQFpGHgcu7M3AytFRIIXsRdEsFhdq9ZkZ601dpkYo3cWVhDqfR6L9GZj9C5vLYYj\nnDNuqdT75lLhkIqPMtJ3hnhkgHCwmWDHERAKNbvfaLrii2pxF9VdljK9U5VPWs4e4jXd3WjCCipc\nG03jOnWIhqIaNKtz1b67S4mzqJR4Xw92+6VE7Ei0CV/98ofp8syMlJLRjgu4naYhZKSTWNw+wqHz\nWL0+rPhIpUKM9jWtehgHMJM37IHVnsVlzeV35ZwrhkRRtBz3ttNRQ6jvIkVVu4DVa7Kzlhq7TKVC\nGKjfz3DHaYykxOLxgDuOryy3repSqvddajr0DzQc+gJWRxEFpdsp3/xqCkq2kUpG6W1+mlCwmVR8\nGITGzoN/ma2mUFTLouewWkwsnwQoDXagCSWnkdH2pOB0xwmou8F8zyp9d5cKZ2EF0uggMtgEukBo\n4K2uw2J1zT44z7KSiAbRlEteNEPXUYQVVXWhp2OomgOLxUMsHITJxRUrj5FGxlY1WfCyZ/0aAhnG\nu7cL/LtzYsSr3WRnLTBVjF7VbPg37p92zFKp940l10lDJxEJUlZ/M8HOoyRjg/S3PEt/6ws4vaVE\nR3tN9TBWNmyyEkxuEAUy8x0d38hoh8UPE7QdLvfvrquoCleRWTJo6GmG2k8R6rgIAhS7RlH1rjl3\n3MyzlCgw/ns1dsmUkvFhbskaSjTPhwYWxfo1BBSBoSdzStCi0RaKNl96sl2tJjtriZlUCKdjIaWN\nYzK9WdGeSBDN5qJk400IRc3W61dsu5PYSCfDvaezYRNPUc2qhE1WgomhGYCe4g2kRs/kNIg6ZRNQ\nfelYr7fv7kDzMVzWeoTTvPEbRppgy3ECdVev8syuPOyuIkZpZEwgT1E1jFSKtBFB1Uz1xGRyZJIk\n+uohcw2XPPNm3RoCmtVBnHZkXMHmKiWSaMZZWpb9IsPyNadZDMuhLDfbPusP/AEnn/j0nFQI56qW\nJ6VBOhHOdtrrb/01sVGzIkZRLdicfhzuS0qcgQ3XZX92FVTl/H45MtfzOFVoxthyM48mY9T1tOAT\nXp53O+ja9sqcTO21+N1dKKlEGFV3IsSlp39F0ZAxFT2dWHsZ6lcAhRu2Mdx+HtVwIIF4vBvV6iIW\n68GQETSPHU/xjln3syIsXlnwimfdGgIAgdqr0NNJ9HQci82TExZYjuY0i2U5lOXmus/aq97GxaPf\nW3Bpo6GnScYGs6I9iWgQpEH1ztcjFBVX4QYcnjJsTj8We8Gqi8YsJ/M5j1OFZoRQSO2+i9PbL313\n1TX+3V0M6WQURXFM2q4odoy8IbAoDD3NcPdZZNIAxcBdUoPN6Zt1nNVRQMmWA6STERACzbILaRik\nkiE0qyubhL0mUDSwr4VkhcuXNXQ2lwdVs+Z0gxtjrTWDWQ5lufnsMzLURu1Vb6Pv4jNzKm10eiuJ\njHTgcJegqFZCwQsM95iCWGMyvTbXpUxeV+GV0Z99vudxx80fnjY0c7l8dxeL3R0gbLRhI1cmVpch\nNNuVV7u/VEhp0H/hJVy2LSiaeakPtbViVKan7aQ4EW1c8qZQFKz2tRIOGIeRhmjv7O/LMy3rVlnQ\n46+T+3/jM5O2r0V1tuVQllvoPvfc/gnSidAktTyXbyPOgkpsTj+J6CDppFlOWLzxRpzeClKJMOlk\neM4yveuNsTbOF49+DyEENlcAzebGM0VIZ+Ix33zdH9L4wjevKGXBiYSDbcT6BnA6NiKRRGMXcZdX\n4CysWO2pXbaM9jVB2ImmOXO2R9PNBOpmzwVaLCumLLitTL543zsXPF69+QvLMq/LiXVrCCiqRW7c\nd/es7u3V1muX0uD4o58gPNhslsRlngjjkUGiQ11oVgee4tqcG8Ng5zEaDn0Bd0aTYOJNY7p9Gnqa\n0b4LSF3HXVyTk6k+fp97b/8kqcQIiUgQq6MQu7uEZHyU7vOPo6hWbK4Adpcp1Wu1+xDK+kneWwiT\nQgFCxeEty/GkuNyl1F3/PgpLt2XHjT/mNXt+h9NPfSnrSZhLaGa1v7sLIRkPER5oRdUseEs25VQF\n6OkEob4mEAJvSf0VaVAuJYPtL2OXk5NJI/FmireY14SZzsdMmNeSRqRu4C7eiMU2uexzxQyBrWXy\nxX9duDifesu9V7whsG5DA9JIrwnlvtmYSllusO0ExOzYHdWkw1H6gi9QVL8nW2M9m7LcVPuMhfoZ\n7biIy1GHECrDF5ux+Zx4SzcBmI13HD7CwSYaX/hX7JlEPm/x1kx5m4eKLXegTci1uNIZHwoAsNg8\nXPXae7HavSQ6jsGR/6Y52s1AuJeGJ/+ObVe9A//2O4Hc84g01pzq5FIz0n2e1EgSh6MKmUrRf+4I\n3upNWf1+VbNRWLFGEtDWAZrNSToUQbPk3qTHmidNPB995w5TuGEztmkaoY0RC/UR6mjD6dhoXkua\nL2Dzu1dPXEjRwJHPEVgM69YQsLtL8VXsnVW5b7X7v09UlouN9kHChd1hxvA0zYlb3c5o53n8taYV\nP5uy3FRqdeGeVjwuUylMT8dQpEaw5Tjp9ChFlVehKBoefz2DHYdJJaNU1VyP3RlAtdgznymyFQB5\nTELBJhoO3YtmdeEuqmWo6xjVO9+A1e5FSoO6pqNcZ92AYanmbGqQ70fOcfbod9ldvAlvYNOk87jp\nwLvWlOrkUpJKRkkOx3C5NgIgVCtu11bCPRewb1q7EtCXM57iWvqGXsClbkZRLEgpicVacVVWkUpG\nSI7Es83FhGrF49pGqPsCtlnOR7i7Hbdrc/Z3l2sjkWAThj+1OuJeRhoiPSv/ueuIdWsIaFbnrF3x\n1kL/94nKcrGRXuw20503XhVxXMdgYGZlufH7NEM/EplSCMXOEQ23EQ61YHeUIoQgPNSeNSQ2X/ce\nXuw6QTo+gqtgbfSGX6tMbBbU9vKPs8fc0FOEBy9SHwqB6kAiSWPwFtcWvhk+xYUXv8H+3/gMQiiT\nzuNaUp1cSiIDLTidk93UMpX3Li0XQigUbz7ASNc5jJQOQuKpqUGzOBjsOI7DvnnSmInXmUmv62nQ\nJ4cP7LYKIkPteAJ1SzX9uSPzvQYWy7o1BGZjrfR/n6gsp6gaRjKFolhym/54crOnp1OWk4aOnk5i\nsRcw1HWCZHyYii13gjAAQTI5wvDAEWo2vR134RYc5Zf2a7V7L3u1upViYvil9eSPsLtLsLUcpr79\nLMXxGIPRAZI2LwOk+Ha4gXe5duBV7YwOtWZDOutBIXAuWOwe0rEwFssEr5JY3+tebRRFw1e1EzD7\nrwRbjiETGsJwMhI7hd1VgsNVnn2/EDPnjAlFQaJP2p5Kh3DYC5d28vMhbwgsiivSEBjv0p0qS9ti\nc1O5/S4qtt2ZzdJuOHTvsmRpT1SW85Zupv/8ETyubVlVRIerCrUg98lpTFnOVViFNHSEohIevMhg\n5zHSyTDJ2AiJ6CDOggqk1NFcFizpMtzeejzeTXh9O4kkmyjybpq0z/WiVrecTAy/mGGdHm5sPEGJ\nagfVSYXqYTQWxury8W73TrZoPko8dkZ7jmZDOlfKMXf6qujrfR5N257NMUmnImjuyfoBeZaHofaX\nsSsbUVzmZd9i8ROLtGOz+1FU65zOhxAKmlND1+Ooqhk2lNIgLQdxuCd7GFaGvEdgsaxbQyCdjGYT\nr3Lj/rtpPfnAlP3fJ7IS/d8nKsspqkZhzVZC3c3INFjcHtRCibekPkemt+vco5kEMkE80o/DU4bF\nXoAnsInoaBf9Lc+iKCr+KlOi1Ve9h6GOBvRoEovTQ1y24a/LFaNZT2p1y83EkM7YeTyd6sevVJrN\nghxFeITKEZHAVlTFz4ur8dccoOnBP82GAq6UYy6EoKh+LyMdZ5FJQEg0tyOfHLiC6HEdxXHpkq/Z\nHDhkJYPBl3B4ytDc9jmdD9+G3Qx1nCYeTYKUCJvAX7/85YjToljAVTb7+/JMy7o1BOLhXrrPPz5l\n3B/A5vRnM+qlYTDU8TJ63HR5qXYNX/Wu7A1/Ofu/jynLdZx5GItSikBBsUHRxp0IRSMVHyEcbKO/\n8SjpRJTh4DGsbh/B9sMABDZci2Z1Z9ZUhM1ZhKGn6W95doJanaCoete081hptbp0Ks5wx5nsTcEg\njsCGkApCA3dpNXb32m0tOjGkM3Yefx3vwifs/HvEbBa021HMaE0dqR23A2CFbChgvSkEzobF6iRQ\nt75yHy4vct3+Qihodicedw2+yp1z3osQyozXkhVHT0Goe7VncVmzbg0BoWjU7nvrpLh/68s/Rk9F\nScaGGO45ha98D8GWY6bLzG4eDkNPEmw5RqDWfJpezv7vRZX7sLvLiYe7SUejOFwVJEYHaHrhe9i9\nhcRCfdi1DQRKb0BaDVTcDA+b7n+3v56qHb81yUOxUo2EFoqUkmDTcdyOrQiHgqGnSMZGSaWHcBea\nyUaj7RdQ6xxT1ievBSaGdIoq9+F2l9If7kVFZJsFdYo00YodjOVSjw8FrDeFwDxrG8WmYhjpHHng\niY3YLlvyoYFFcfnVIc0RV2E1ldvvyj6xjcX9HZ4ys9udzUPDoXsZ7m2AhNVscmLoDA0cQwgVGdfQ\n04ns/krrbkYoliXt/66n40RHuij070cIjdbG++lp+znhkUaEYUWzeXE4a/D5940bEybY9TxC0aZt\n+iOEwqZr341QLJx//j4GO49Nm4xmlk8em1MjoaUiOtKFTavIfo6RTmG1FmLol1KWXc46Qn3NyzqP\nxeD21yONFH3NTwPmMa+/6U8RQuV7sfNEjTTH7QrPb7sOS2FldtxYKMBiL1jRY54nj2/DLmJ6M9Fo\nO4lYP+FoI46SQE4jtssSCRhy4f/yrF+PAEAiNky4rxUMgeaw4S3dDEgcnjJq9/8eDYe+QNPh71C7\n6Y8AcrL07a5y9FQs2/BkqbK7o6NdxEa6iEcHSCfCSCkx0nE27Xw/Fxq+ykDvs1htAYrLX4HNGUAz\n0khp0NvxJP3dT5GI9yOExoadb54xcdHjr2fnwY/QcOjeBTcSWi5S0REslvJJ28U4u1QIBfTF/ZFK\nKQn1N5OKhEGAy1+J3bM04YapmgV5A5vYectf0nDoH/h+9Bx2ZZjyWBWlGa9BMj5Ke8NPABjsPLqi\nxzzP8pFKRgj1NCF1UCwqBeVbUdS1d2lVFI3i+gOkk1HSqSiFzrp1YoBKZN4jsCjW3rd1iTD0NKHW\nVhyOGoQq0GNx+i+8lHXpuv312D1lRIfbGBk+ht3xmmyWfkHRbiLxZizj6mznm90tDYNkfJhEZIBE\nbIhA9QGEUIiHeomOdmFz+XH7arG7Aox0NOF2bGXr3o/QduG/iIZb6Wx5kK62h7DYCkjFR5AyDYDT\nXUNZ9Wvw1++edQ6+8j1rUq3OFahhuLkpK2ZCJot8fFlSOhVB8ywuLBBsOY7VKMFhMW/+ka5O0oEo\nbv/iM/SnC7+Yx/yTUx7z6GgPSHONrqI6Nl/77rwRcJmTjI0w3NKIy1mPUBWMdIr+xhcp2XLdnOV6\nVxrN6kSzOmd/4+WCYgH35AeLPHNn/RoC6SRO58bs76pqx6qXY3eVEgo20X/xmWzcP57sIRK9iNNR\nQ4F/D9FoC86Skhwp3blmd8fDfYz0nSERHUQa5kVfs7rQU3E0q5PCst0ZdbhL+/aU6YS6GnG6atm6\n9y/p732KWLyFZCyIrqdQhIbbu4VC/x5sjgA4EnNOpPNk+hGsJbU6i9WFxWslFurEbq9AqCpDAy/h\ncJoiRsnEECmln0DxwuW/E9EhlKQDzXGpn4LDUUlksGlJDIGx8MuJxz/F+efvyylDnXjMQwMXSCcj\nCEAKhbr9b6Ni22+sk6exK5tQz8UclT1FseCybWakt5HC8m0zjMyzZBgpGO1a7Vlc1qxbQ2AsQXa8\nOp/VWoDDY95suhufYM8dn6Ll+A9IRPoovG4zob6LCASF9VuxjLOYp8ruTqdi5tN+JEgiOoCvfA92\nd4np6tfTuH212Fx+swud5VJt7lQuQ0dBCf9/e3ceJEl2H/b9+8u7Kququ/runp6Z7jn3xO4CuwtI\nom2IhwRADEFyhGEodMAyg1DYlEnKtETQ/MMUGYpYWzQpOWTSWoOyYUsmheAhwjRNGoDJgG2KwB5Y\n7D3TPdMz0z3T91X3lfn8R9X03dMzU133+0RUdFV2ZeWrV9fLd/x+jt9Pd9KCCgAAIABJREFUeuUG\nYVjhzEe+Hyeym+5ThQGplVmCcplI/+BDpxC9rx2j1fVPPEExv0V2bR7Dtph44ZNkN+9SKMzjDiXp\nT75U1+Pntu7hekdkrjshctqjOGn4JT50hVxqkc3FtylklhHD5pkOTRbUzpRSZDduUy5kiQ2dw25i\n6mIVAAdO/A3TISyWmlaGnnd/joD22Lq3IVA74d477u/3XWBg8jk2Fl8ns3GTzPqNnXF/2/EZmDx6\nSczG3bd2ZnfHhy5z98P/k0opWz2MYeJGB3cOGImPEomPPnJxTcuhf+LJo5+KYdI3dvWRH7PduZF+\n3LO70cgSw6cXnjQSHyW/uIbrHUhGYp3uF0a7Dr/0inIpy8bNt4nY53CsCbbnbmElrGM/S6ftqE6d\nMCxjON371dpuFKDqnE/U67r23WpYNvnC3Z1x/3j/k+TKNxgZ/MROl+61f/s/EJSy+ANT+/ZVYUAx\nv0khs8rG3TervQFicumlv41l+zjRAeKDF3H9IRyvv+fT8LYjLz5EemUOK+jDNKsTPouFFbxk8tSP\n1Y7DL71ie+E6schutMJo9Bz51F2KyS3cSOND3voj50gvzOFHp4Fq72EmP8PI2fp6tLRHYNgQP6L3\nT3to3dsQMG2i40Pk1u7g9vVTdpYZnnoJEdnp0n33j/5rUCG57Xvcevs3OXP1U9hujPn3/3e2l98n\nvXGToJQFMbn88R/ZOZsbPvfxFj877WEMXfgY24vXKBWKKIHo8DDR5JmTd3wM7Tj80gtUSSHR/eG3\nvcgE2fV53MnGNwS82AByziCzMgeBIBYMXXqhLVcNdK2gBNt3W12KjtbV79ZIfIRI/HCe6koph+0m\nsByfSjFNWMkz/85vsfDe12qzu5ehNktfd+l2LhGjaV3EWosc0cmiwjKm6zStCG60H3dKB4RqKT00\nUJeubghAtauuXEhRzK0TG7iAiLC98gGrt/6ESjGN6w8zdun7a+v77wKK+MB53aWraR3AiflUChks\na3d1SLZwk5Hzj7/iROswCgh0HIF6dG1DICgXWJn7fyhm1wnD6tm96w9juzGCcoHNpXdBLJ78d35C\nn+1rWofqm7jK5t33KWaWqz8IlqL/3JW2XcOvNYACpVcN1KVrGwL5zDJr868xdPbj+P2TGFaE5Zvf\nYmn2mzuR9J7583+/bRoB20vXKaUzgGA6Bv2TT7dsnDEMymwuvE9YCgGFk4jTN9qqFKNaGJTYnH+f\nsKwAhZtIkBi9dOJ+vSJ5pgti5R+w7zUXhRvXr/mxTBtJNGbuT6/o2oYAKmR9/jU27r7V9ku5Nhfe\nxyz14bvVZYcqDFi7+QYjl1szKXFt9g187yriVodEypk0W8GH9E/oACmtsDb75oHXI6Vfjy63Ovsm\nsb2veTrFdnidvvErLS5ZGwpKqM2FVpeio3VtQ8CLjZKceK7tl3IppahkcrjR3RatGCaWGqCQWWt6\nKt7c9iKOMbavfmw7Tja1DHqFTtPltu4d8XokyKZW9OvRpbJbd3HN8f2vuVN7zXUk3cN0QKG6dW1D\nwHKiXHrpb7e0DEGlyPbiDAQhhmvTN3rU2KVChYcbJY7dTym72fSGQCm7ie0c8W0T7i7RyqeWyG0s\nAxAdGCeSOLwyo1mUCkmt3CDI58EU4qMXsJ32TF38OIq5TRzniF/8UA5v07pCKbeFY+vX/FG0co6A\niAwA/xqYAm4Bn1NKbR5xv1tAGgiAilLqxUfZv5Ha47S4C5WLGdZm3sINJ/CM81iFIVZmvrOTf+A+\nEQM5ItpdvngPf+Bss4q7wx88R75wxJpcu1rG7cXrFJbSRIwpIsYU+cVNUsuzTS5llVKK1dnXMLJx\nPOM8bjjJ5o0PKGQ2WlKeRogd+3o0vyxac/gD58jnj3jNu/a0rU73Vw087qV+XwK+qZS6DHyzdvs4\nf14p9fz9RsBj7N8Q+q3VIKnFG8SiV3cinhmmg+9cOjIZiT8yTnbxBtHodDVDYX4RO+Fg2s3PE267\nMUxfKOSW8bxRlArI5uaIT0wSBhVK2xn86O7cCi8yRmZzlvhw2PQIi9mNO7jGBKZVzeUgYhDzL5NZ\nvokXG2hqWRrFduOYvlDMLePuez2a30jUmsPx4pixg6/5TeKT9SfL6kqWjfRPtrIEnwU+Wbv+FeCP\ngZ9u4v510w2BBlGBQuz9XXnHJSOJ9k9gR/tJr9yEUBGdnMDzB5tV1EOSk0+TT6+S37wDpjB4+WlM\nyyOfXsE2DofotSROuZjCaUJI171KuRSeffjLsZLPs7X0IfHBKcQwSK/OYTpR/OTkvqyPneK410Pr\nLkop8tuLlAopEmMXKeXT5LfuIIbB4OVnMS231UVsT5USamO+nkcYEpHX99x+VSn16iPsP6qUWqxd\nXwKOSzajgG+ISAD88z3HeNj9G0Y3BBrkqN8bFQZgHX3WbDvRY5MetUIkPnwoy6ET6SMTLuKw/2w7\nUFksJ0azmY5HkM3v9AgoFVIp5gmKZazCMIvv/H8YhkOi/0mCTJ6V5W+TnH4ax2tedrrTctTroXWP\nSinP+s3v4VrjWNYwG7Mf4vRHGTj7bKuL1hHqnCOwdqCr/hAR+QYwdsS/fnZfOZRSInJcYb5PKXVX\nREaAr4vIh0qpbz3C/g2j5wg0iD9yllzu9s5tpRSZ/Ax9Y527Ht+0XMwIVMrZnW2VSgbTt1oS8yAx\ncpFc8SZKVcf5glKBUmEdzx8HFWJJglj0MoKB7SSIRZ9ge+F608upaSfZWviQWOQqjtOPYVj40WnK\nWyVKhXSri9b+FNUQw497eZhDKPWDSqlnjrj8LrAsIuMAtb8rxzzG3drfFeB3gJdr/3qo/RtJ9wg0\niBcbhEnIrsyhFIgJAxee6fjuveS5j5Bauk4uV32v2rEoA6PNP2tRKgSlGLr8MbbvfkhYVuS2Fokl\nLuFFRkhvzxL1z2MYFpVyHsuJImKgyk0vasvdr6tmRtsLwwoiZkcOxbRCNXnS/vOySGSSzNrttuop\nbEumgyRbOkfga8AXgFdqf3/34B1ExAcMpVS6dv0vAD//sPs3mm4INJAXG6w2CLqIiNA3frVlxw+D\nChu3v4cqSrWBZYf0TV7GifSxGoR4XnUpo2G6FIurlIubiOGAKJAQw+2dt/z9ugqLgJJ9ddUoua17\nZFcWITBRBFgxl+Tk07pBcJIjhxLLmK5eHnKiSgm1VtccgXq9AnxVRH4EuA18DkBEJoAvK6U+Q3Xc\n/3dqnwML+N+UUn/woP2bqXe+FbWusHH7e0SMacTfPbvduv0hw1dfxvI9KoUslu0TiY6zdu/fMjj0\niZ3VDGFQYjv3ZquK3nQPqqtG/DCXSzmyi0v4/m4o3EopS2rpeksbj53AinlUijksK7qzLVeYY+ic\nTmv9MFoZR0AptQ78wBHb7wGfqV2/CTz3KPs3k24IaB1DKUVYZN8PG4BjjZHbvkf/xJNs3X2fYmaR\nQmaNRN9VQlWEwAABMQXXa/qE3JZQKjy2rvLbi0T7Tz8sYXrlJtHo9L5tlu2Tyyyf+rG6Tf/Ek2wu\nvEcxe68aOMhRJM5eaFm+kY6iHn6sXzuafpdpLRGGFbZqiY0UCq+vn/jw9Al7qerEoAMMwyGs5BER\nkpNPA7C1dA2rkMQwDnStdsgcgczGPIXNtVqXvtA3cRXrUeJKKDiqv9kQm6BSPLVy7j+mOvKY+iv6\nZCLCwFk9F+BxKHT2wXrphoDWEmuzb+A7lxGnesZa2t5iO7hO39jxSVVEDMQ5/IEvFO8yPPDCvm2J\n4QusXX+b2J5uaqUU0gFDrum121Q2SkS9C0D17H79xpuMXP3EQ+fIEMNA7MNR0wrlewwPNKa72R88\nQ2b+HpHIbt4MFQaYjl6cpDWOWA4y2NLJgh1PNwS0psunlrAZ3jeL3XH6yW7PHr1Sd4++ySts3foA\nxxzBMFwKpXv4o2OHZsQbpk10ZJjMygyee4YgKFAKlxmYav912YXNdXxvN3qjiEHEniK9dovE8IWH\nfpyj6io2Nt6wCJBudIBCYo3s9hyeM06lsk1Zthi6oMe5tcZR5RJqpaWTBTuebghoTVfIrOO4R4xR\nBydPYHO8BMNXXyafWiKslBhOfvTYH7bY4DmiyQmyG/N4ToRk4hP1Fr05jkguY9k+hcKj5VDYratF\nwkr5gXV1WvrGrxAMF8ltLhCJDpH0uz9tbhhUSC3PEJbLWBGf+PB022Q37Rl6aKAuuiGgNZ0/cI70\nrTtEoge68x6y215EiPY9XD5Ww7CID50096C9yBGfymJhjcjYo0cWrNZVc/MVm5ZLfPjiyXfsAuVS\nlo0b7+BHLmEYNpV0lpWt7zBy+WXdGGgapecI1Ek3BLSmc7w44ocUC+u47iBKheTyt/HHThgX6BGx\n0fOk5mfwoxcRMSiXtgmsLSKJ3vhx7SSpe7PEok/sLMe0bJ+oTJFauUHfaOdGEe0kYjkYQzoJVz2a\n3mQVkbMi8kci8r6IvCciP1HbPiAiXxeRmdrf5J59fkZEZkXkmoj8xWaXWTt9A2efxR32yKs7FI0F\n+qcv7lvSplRIdnOe7NYCSrW+tR9USqTXblLINj7FsRcbYPDSMxSNuxTUHcykYnC6c8bZg0qxaXXV\naqqiDsVkMK0IQT7fohL1IAWE4eNftJb0CFSAn1JKvSkiceANEfk68B9Rzcn8ioh8iWpO5p8WkaeA\nzwNPAxNUszddUUoFLSi7doqi/RNHrmfPp1dJ353DtcaBkJWl1+ibvNiyKI3bS9cpbebw3DHyG2uk\nZJahCx9t6Bpv0/Y6cjnZ9uJ1Sts5PGeMXJPqqqWOOJV6UHIx7fSpSolw+U6ri9HRmv5uVUotKqXe\nrF1PAx8AZ6jmZP5K7W5fAf5K7fpngd9QShWVUnPALLvJGrQuo5QifW+OWPQqtpPAdvqIR6+Sunuz\nJeUp5japbFfw/WlMK4LrjeLbl9haeK8l5WlnxdwGlVQFP1qtK68H6sofniCfX9i3LZuf7ejkYh1H\nVeMIPO5Fa/EcARGZAl4Avs3xOZnPAH+6Z7eF2jatCxVz61i7o0I7TOVTLqax3eamEM6uL+B5+8cf\nxTAJCrpL8aDs+t2j66rYvXUViY/AmCK7fhMCQSxF//STHZ9crLPoyIL1allDQERiwG8BP6mUSu0d\nZ3vcnMwi8kXgiwBudOi0itqxMut3yK+vVQdjTIWb7CcxcvKEs1J+m+27M1AyUKKwohbJc882ZRa0\nYTqgKoe2KxU0NXveDhGqg5ByeLO233F11ZLC1Ce/vUxmZR7KBpgKKxYheeapI+8b6Rsl0nd06Orq\nZ3AVKlL7DCZJjDx8LIh6HXwedjxK/8STTTt+U1guMnKu1aXoaC1pCIiITbUR8K+UUr9d27wsIuNK\nqcUDOZnvAntPMyZr2w5RSr0KvAoQH7zQ003EQmad4loGP7L7w1/aXiNrL+A/IGWnUorN2x8Qjz4J\nTnVbGJTYvPMOA+ePzJlxqhwvQUU+rEYBrP3aKhWirAKWHT1h79MXH7nA1o0P8f3deqyUs1h+pOll\naXfH1ZUda/7rVo+gUiB9b4GYfxlqJ/blQprtpQdHvjwon16luJbFj+xGtyxur5Kz7xJNNr5T88jn\nkX/059H2ykXCpdutLkVHa8WqAQF+DfhAKfVLe/51Pycz7M/J/DXg8yLiisg0cBn4TrPK26myawv7\nQr0COO4Q+c21B++3cZuIvb971zAdKvnDZ+mNkpx6mlx5lkz2BtnMDXLlGyRbFBHQdqL442NkC7Nk\ns3NksrNUnA2dTe8Ix9VVosN+dLaXZvEPJE+y7TildOaRHie3fo9IZP9kWNcdJre5WncZH0b1eezv\nfXic59EJVKAe+6K1pkfgzwF/E3hHRN6qbfsvOSYns1LqPRH5KvA+1U7uH+uGFQPlYprU4iwqEMSA\n2Mh5XP/w2PhjU0d3yJ404FIpF7DMI8bhm/h5sR2f4UsvompLexodDe8k91c3hEEFMcyGpPDtFo2q\nq/TaLYrbm4BgeQ59E080bqgqVIgcMQx1zGeqkFkju7KAUiAmJCYuYzv+sZ+ZemtEKcX20jUquQIA\ndsw/OmaBUsfUUXe9f5XSSYfq1fSGgFLq/+X4d+KROZmVUv8I+EcNK1STVUp5Nm9+gB+9gtjVqkjN\nz5E4J7jR/lM5hh2LUU6lsO3EzrYwKGFGHjyJKT50gY2Zd/d17wItSdbT6gbAQV27BK4BTrOuthev\no7IuUad6dhuWSqzdfJPhiy+e2jH2iibHyd1bxIvsRq9UKsRwD78fC5k1MncXiUama/dTbNx4h+Er\nH8X2Y1TSGSw7tnP/IChievVNJNy48zZOMIpjV4enypk0m+X3djJv7jyP/nFy95bwIruBupQKMbow\nCVSoz+zror/ZWiC1PIsfvbzvbMmPTpNZnsOdfv5UjhEfmmYj+z0q+TSeN06puErF2GZo/GMP3M+0\nHLyhJNm1G0Qi5wgqeYqVe/RPddkEI60jKKUobafwo7tnvIbpYJZ8ivkt3MjpNJz38uJD5GMrZNO3\niUYmKZW2Katlhi4e/uxkVuaJRna730UEP3KZ7cUZ+s88xXr2Lcr5FJ43RrG4SmCmGBp//OBQQblA\nmBfM6O4cFduOk02vEAaVfQ0wLz5E3l8hl7lDJHLmgc+jo1kOxtj5Vpeio+mGQAuoQB15tquOSDbz\nuESEwannKea3yG8tEhkawXvIBDDx4Wn8gTOkV+cwnSjDyZd1d7jWEkoFoA5/TdnOAKXMekMaAgDJ\nM09RLmXJrt/B7U+S7DsmYdURn1nDsFCVABFhaPqFnc9gdGj0oT+Dxynmt7DNxKHthkSplHM4B/6X\nnHyK7eUZtlbfxI74DE291H09W+UiweKtVpeio3XZO6IzmLZFWC5jGLv97UqFGA14NdxI/2N9WRqm\nQ9+YnhCntZZhWGAcnhJULK7Qd6axy/Bsx6d//ME9YWKxb4ULQBAU9g3BPe5n8CieP0Q2eAeH/fOJ\nQrLYbuzQ/Vdvvo4dDpNMvEQYlFi9/jrJ6adxvObG42gkBTvzibTH032DRR0gMXaFbOE6YVACqiFJ\nM7lrxMd1NDJNOygyNEw+v7tiuFTcxPClOiGvxeJjF8nkrnF//nIYlMiVbpIYacxn2TAt7ESEYnF3\n5UGhsIib7Ds0MTC7uYAdDuM4/bV9HWLRJ0gvzjakbC2j9KqBeukegRYwTIvhKy+TWpohLJXBMhi6\n/FFMy2l10bQ9CtkNwqBIJD566Eu2lN+mXMwQTYy1JtBRD4kNnsPyNsiu3QIleINJIn1PkN26i+3F\ncbzDXeXN4nhxhi4/z8bCOwT5Am68n5HzH2/oRNf+iSfIbS+S37oNCqLjY9UIhwcUM5t4zv5AOyJC\nWG5Y0VpCbAdzXM8RqIduCLSIYVjdF+GrS5SLWTZvvYvFAIZhk7n3BrGxSaL944RBhbWbb2IGcSzL\nZ3XxLSJDA8SHp09+YO2xef4Anj8AVCfbrs+8jWMNUwjmCc0cQxc+1pJVJkqFbM6/ixQ9ItYopfQ6\n23Kd/oknGnrcaN840b7xB97HsGzCQqkarXPfP7rrLFiVS1Tu3mp1MTqabgho2gFbdz7A967ujPs6\nJMksXiPSN8rm/Lv4zqWddea200d2bQ6vL9sWXdXdrpjforRZwI9Wl7faJAjDMpsL7zJw7iNNL8/2\nvWu4TGJGvWp5nATFzAr51BKRxNgJezdWYuQSq9dfJxZ9Yue9XCysEBnssvDrSnfx10vPEdC0PZQK\noWwcWiXhOZNkN+YJi4eDzUSj50mvzDWzmD0ruzZPJLI/RLZh2ATF1sQYq+SLmKa3b5vrjZDbWDlm\nj+YxTIvk9NMUwltkCzfJlm5iD7rEBrsvLr/OPlgf3SOgaQeEhKQ2qvkOqrGvKriREVzDOzo0o1IY\nXTxPIAxKbNx+l7CkEEBcg4Hzz1Zn9DfdgxMblYtptuavoyqCiMKMuiQnn27c8tfjHvbRc6Y1hOPF\nGTyl2CTtSgGh/kGvi24IaNoeIgbF/BL9sRd3xlaVUqxv/Annr/4wxcwWYWX/uGs2P8fgZGtyITTD\n2s238J3LSLTagajCgI2577UkME18ZJqtuZl9uQCCSh4z6laj+t18r9oV7lR/oYNyns2F9xg4+0xD\nymPHfMqZDPae6IH5/D38ybMP2Es7TWK7WBNTdTzCt06rKB1LNwQ0bQ+lFG5kFAxFGBYBQRGS6HuC\nQnqZ5OTTbNx+mzCrMLAJjAKx8YmuXfFRLmYwA3/fqgkxTFTeIqgUMa36wuU+Ktv1iYwMklmdwVQR\nQlXE9E2SEx+pJsxyzu47+zetCIVssWHl6Ru9zGb5PbKZZQxcQgp4g7sTG7XGU6Ui5Xk9NFcP3RDQ\ntL2UQjCx3Gh1EhLVJVdGxaBSzCIJg8Gp5wmDCmFYaklq5GaqlHMYhndou2E41dwVTW4IAMQGzuIn\nJwnKeQzL2RmiqBRz2NbhCXqqwb3GycmnUWFQbRjZXuOSIZ2CfGqZ3NoiKBDXIjnx5Kkvf71/jJ//\nhQKXLg7yuc99Gstq7E+N0vGE6tK+71hNawExDMSq/XKI7JxdFor38Ad21yobptX1jQCoRrIrhxuH\ntgdksZzDkeyaRUSwnOi+eQrx4Qvk8ncO37cJCbPEMLGcaFs3ArIbC+QWN4hY00TsaZzKGCszr9Xm\nwpz+Ma7N+nzt99P8Vz/36qke4xClJwvWq33ftZrWIvGJadLZDwmCAkoFZLNzeEPJ7ovR/hBEDPyR\ncTLZWcKwQhiWyWRniI9Ntl3+CdP2cJMxstlbKBUSVPKksx+SOHPx5J17QG59hUhkYue2Ydh45hly\nm/OneIzlfccwTZuZmw6vv/69UzvGQQqFCsPHvmh6aEDTDvFiA7hXXyK1eoMgKDNw+cmWdIG3C39g\nkkjfKKmVWUQMhs4+37aNosToJcrJHJm1OcyIx8j0S219lt5URyRIsp0Ehdw89U5pyKeWyW+vUs7l\n4MBHxbb7uH79cE/NaRHbxZqcquMR/uS0itKx2vPTrGktJoZB36jO/XCfYdonJuBpF7YTJTnxdKuL\n0X7Mw93gpeIm3nB9rYC1W9/FLCfw3LMUKluUC1ksdzdNcrm0wTPPfF9dx3gQVSpSua0nC9ZDN5U1\nTdN6QGx4gmz+9s54fVApUJJlov0TJ+x5vNzWPaxyEtetRiv0YqMUcktUinkAKpU8H3nG5LnnGtsw\na+UcAREZEJGvi8hM7W/yiPtcFZG39lxSIvKTtf/9nIjc3fO/z9RdqEekewQaLKgUKRdSONHkIwVg\nUSqkmNvAcnwsO3LyDprWRGFQoZTfxPb6unbpZLeJ9I1h2B6ZldsQClbUZXj0pboes5Bex3N3IxV6\nkREMw2V7+y2ee3aUp56c5DOf/mSdJT+BanlAoS8B31RKvSIiX6rd/um9d1BKXQOeB5BqaNK7wO/s\nucsvK6V+sUnlPUQ3BBpEKcXmnbcJ8grLiJMO53H6Y/SNXTlx3/TqHIX1dSwjSSW8hzhlBqdf0GOd\nWlvYuvcB5e08ltlHOryL6ZsMnO3egErdxI324071n9rjGaZFWKrsO8lx3D4i/WP8g//ir5/acR5E\nHAfrbD1Jv75dbxE+C3yydv0rwB9zoCFwwA8AN5RSt+s98GnRDYEGSS1dx6oM40WriWhchiimVshH\nH5yMpFRIU1hP40cv7+wXBqVadDT9Zau1VnZzAZV18f1qd7LLEOVSmtTKDRIjenZ+r0mMXmL1+pvE\n/d1si6XiGl6yeQGVVKlI+fbNeh5iSERe33P7VaXUq4+w/6hSarF2fQkYPeH+nwd+/cC2/0xE/hbw\nOvBTSqnNRzh+3XRDoEHKuTxRe/8PfjUZya0HNgQyq7eIRvaHJzVMhyDfZUnEtY5U2F4n4k7t22bb\ncXLpNRhpTZm01jFMm/6pq6QXb6LKgKHw+pPEh6aaVgalqDf74JpS6sUH3UFEvgEc9cX9s/vLopTI\n8YkmRMQB/jLwM3s2/yrwC1TTJvwC8N8C//HDFf106IZAA4WVEmFQ2cmRYlgOnLj2ur3WZmuNoZRi\nc+E9glwJUIgtJM893fHLFIvZTdJLc6iKgKFw+/p6qqcgDCpszr9LWAwAwfRMkmefRYzuHdZzI/24\nF15oaRkaHRhIKfWDx/1PRJZFZFwptSgi48CDUk9+GnhTKbW857F3rovI/wj83mmU+VF077uzxUJV\noFRIY4iLYbgY4pLdvo0Te3A0tvjIFLnc/jW3YVDCjOgJWd1kc/5t7PIwfuQifuQSEXOa9RvfbXWx\nThTpG6ZYXNu3rVxO4SQShEGZ7TszRO2L+JEL+O5FwpRJerWubtuOsjb3XTzO4Ucu4Ucu4qgzrN9q\n/9e1o9V6BB73cgq+Bnyhdv0LwO8+4L5/jQPDArXGw31/FXj3NAr1KHSPQIOIuJTKa5RKG1imT7mc\nwnRcyrn0A/ez3RjecD/ZtRlM6SMIc4gXMDjZ3alEe4lSikq2gufvxvAXMbBlmHx6hUi8ffvYo8kJ\nSoVrZLdvYkqMQGWwYg7J4afZuvcB0cj+s3/HSZLdvkF8uEUFbqJyMYNZiSLObux+w7BRBZOgUtKr\nKxpEXBf7/IU6HuGNeovwCvBVEfkR4DbwOQARmQC+rJT6TO22D/wQ8HcO7P/fiMjzVPuObx3x/4bT\nDYEGEYRE8iphWCao5IgkxhAxyIcnTxSND50nNniWcmEb0442pbu4kNkgu3YHQsH0HPrGrnZ1d2ZL\nKQXq8BCQZcWoFNLQBg2BUiFNevkmhIJYQv/E1Z3Uy/3jV1GjAeViCsuZ3okyGFYqRy+RPSKiXTeq\nlDKYxuH8E4Z4hJWCbgg0iCoWKc3daN3xlVqnuhLg4PZ7wGf23M4Cg0fc7282tIAPQX/TN4hhGygV\nYBg2ttOHiEGlnMH2Hy5Ri4iBE0k2pRGQ314is3CXiDFNxJrCKg6xOvudxiYK6WFiGIh9OMZ5oXgX\nf6D1eeyL+S225q7jcZ6IeR43OMPq9dcJw8rOfcQwcSL78y9E+ofZ8GteAAATUUlEQVQpFvYPGyil\nMJqQ9KcdeLERyuH6oe0BGSw33oIS9Qj1+MGEdNKhKt0QaJD+M0+RKVyjXE4BUCysU+Qe8aF61rs2\nRnb1HtHoblAQw3RwjIlTTUai7RcbO086e50wLKNUSDZ3G3cgsXPW3UqZpVvE/Es7SYXEMPG9y6SW\nZh64XyQxhnIzFArVuU+VSo5M7gMSZ642vMztQMQgMjxCNnsTpUJUGJDJzhIbGW+7BE3dRKGzD9ar\np4YGysUMmbXbGJZDYvjCqefh3sswLUaufILsxm0K+XkiY0NEEvWMYzWOCg5/STlOH4Vs/clItKNF\n4sO4V5KkVmZRQUD/9AVs9/TT+oZhhfTKDcKgQmx4CtvxT9xHBcCBj4ZhOoSlk5ewDpx/jkJmjdzW\nPFYswsj0x1saCEspRXZznnIujdc3QqTBkxVig+eIJEZIrdxAxGBw8lk9JNAELY4s2PF6piGwvTRD\naStPNHKWsFRm5drr9J29jBdr3C+diBAbnGrY45+aI5KRlMvbOMlECwrTOwzTon/8iZPv+JgK6TVS\nCzeJetNYhsXWjRncwfiJy/nkiPZxGJYxnIfr4/diQ3ixoccp8qkKgwqrs6/hWZO49iSFxRWya3cY\nnPpoQ8/QTdsjeUYnPWoWcVzsqTqWqH67cSmSO0VPNASCSoHSVhY/Wu2WN02XuP8EmaVZvEv6lNcf\nGie3PL8TyCgMyxQq9xhJfrzFJdPqkV68Rczf7Zb3/Wmy6zcIB8sY5vE/6rGRKVLzN4hGLiAi1aGL\n/AzDZ+uLS99sWwvvEfOuIrWWjeuNUC57ZDdud0YDXXsoqlikeLN1kwW7QU80BNJrt4h61TFwpUJA\nEBHCUmOOVy5mMEy7Y4LDRPvHMSyb7OotUILpWoyce4lKKdtRz0PbpcIAVTncJe+5E2Q37hAfPv4M\nyvX76Zu6RHp5DiogtsHQ5Y/tmxjYCcKSQtz93Ru2nSCfuU3s0NxtrVMpIDw891Z7BJ31yX5Mthuj\nlNrCwEMwAQUSVi+nKJ9eJXPvNoaKElIBu8jg1PMPPPtqF3u7c/PpFVZn3sRU/p7n8ULH/RD0NDHg\niEinlUoW7yFmsDtegsHzzzWiZM1jHH7+SoUnB/fUOovSDYF69cQ3eyQxztq132dg8M/ujA0GlTyF\nwuqpHUOFIemFuX1dsUqFbNx+m6ELHzu14zRa9XncOuZ5fLSFJdMehYhgRS2CoIhpVnt0lAopqTWS\nid4I+RtJDlFcX8V1dycI5vK36J++3MJSaadNXBfnQh3v6e/qOQI90RDIbtyiL/ksmcx1qismFYZh\n4rqnF7glsz5HZE9ebqguJwqLp3aIpqg+j/P7tlWfh25yd5rkuWfZXHiPQq5c7QRzYPDCR1pdrKbx\nB84SBnNkt25AIIiliI1PNmR1htY6YbFI4YaeI1CPnmgIqDDEdOL0RZ7atz2TnT21Y4RhiNHCZVKn\nJQxDzCOfR7UnRYUhmwvvEhSqwWXMiENy8qmWLhHTjiZi9Hzq6vjwNPHh9ovdUY/qZ/AdgkIA6M8g\n6KGBevXEOyc2PE2+sD+Rj1IhcorLe+PHHMPosCXE8eFpcsX9YZD3Po/1W9/FCSfwvYv43kWcyijr\nt95qQUk1rTetz30XJ5w88Bns4e7t2hyBx71oPdIjYBgWsfEJMkvXsY0BlCpRkRQD06c3GWrnGMsz\n2JIkDEsExukeoxkMwyI2tltXe59HpZyDooPh705+NEwHlTN0UhVNa4JKKQtlF8Pe/eo2TAeVFcLg\nwctCu5VeNVC/nmgIAET7J4j0jVPIrmJaHo73ZEuPEQYVtpeuEZYDxBT6xq+0zTK9vc/Dsjzs2vPI\nZ1YwzcNJVUyJElTyuiHQ5pQKSS3PUCkUEUMRG7mA4+kY+J2kXExjmYejQxqGR1DO92RDwHBdvIt1\nTBZ8v4d7U2p6piEA1ZnUkVhjM7s9zDHCoMLq9dfwI1cwDAsVhqzNvMXAxWceKgRsMxz1PDx/iEw4\nj8v+qHGBSmO7eiZ2O1NKsTLzHaL2NBHTQynF9twM8bNTDY2uqZ0uzx8hHXwXh/2vWS8nNgqLRXIz\nerJgPXpijkC72V68ttMIgOqkrlj0KqnF05u82AgiBt7gANnsrWpSFRWSzc0RGR7WSVXaXHbjNp4x\niWl6QLWh5/sXyaycnBZbax9iGEQGB8jmbu9+BrNzRIdGevczqECFj3/ReqxHoF2EleBQ3nYRQZ2c\n06Xl4sPTeIk06dVbgNB/4XLb9GJoxyvl0njOuUPbVeWIOzdRbnuJYnadaP8Z3Gh/awvTIeLD07jx\nNJnVWyBC/8Xe/gzqOQL10w2BFhBTUGF4aLmPHJH8px3ZbpyByd5eltZpTDdCkMljWpF921v1ngvD\nCms33sBhFMc5Q3Z+kYx3u/OjGTaJ48V7fmnoDh1ZsG66IdACfeOXWZv5HrHo1Z3uvGzuBonz7Zmm\nWOt8ieELrGz8KTHzyZ0GaD6/gD820ZLybN39gKh9aadnzItMUCptk9m4Q2zgcM+Fph3H8Fwil+qY\nLHhTTxbUDYEWMC2PgQvPkFqaRZWrZ2WJ8xdwI7prVGsMEYPhyy+yde8aYSlERBGdOEMkPnzyzg0Q\nFgMMZ//Xj+P0kU/f1g2BNqVUSFgpYVhOWwUvCgpFsnqyYF10Q6BFbNfX3aBaUxmm0z7dyUckRFJK\nIUds11ovtTxLcXMLwUNRxOmL0zd+pdXF2qGUft/UQzcENE1rOq9/kNLmOo6zmw84n79NYqq7wgF3\ng9z2EsG2wo/uLhEuZTbJrN8hNtgevTd6jkB9dENA07Smiw2eIxXMkt2ahUDAUvhj4zheotVF0w7I\nby4T8ab2bXPcJLntubZoCBiuS7SeOQJ33j69wnQo3RDQGqZczJJavE5YBhGFNzBMbOBsq4ultYnE\nyCVobHwv7TQc0+sutEfcgqBQJHNdzxGoh24IaA2hwoD1m28Tjz6JuNUvjOL6ChnVPt2JmqadzI33\nUdraxnH6drZVylksP/KAvZpLDw3Up32mfmpdJbUyS8y7tC/ameuOkN9ca2GpNE17VLGhKUJ3m2zu\nNpVyllx+nrK5QmK0TcKK6+yDddM9AlpdVBiwvTxDWCxh2BaJscsYpk1QLmGbRyQhCtujO7FRgkqJ\n1NIMqhJgei6JkcuIodvbWnsoF7Okl29CqLD9OLGhqYcKTTxw9lnKpRzFzBp9/jS22z6RDHVkwfrp\nhoD22MKgwurMa/juJQzTISxXWL3+OoMXn8eNDVBa28Rxk/v2kS5+x5WLWTZuvEssehkxTIJ8gZWZ\nbzNy+eO6MaC1XD69SmZhnmh0GjEMytsp1tJvMHzhxYfa33ai2G0Y48HwXPwrdUwWXNGTBbv4a1lr\ntNTyDL53GcOopj41DItY9AlSi9cZOP8cq5uvQ8nAcfoIwwrZ/CzJqSdaXOrGSS3OEvN3o0WapkfU\nuUBqZYa+sastLp3W67Ir8/j+7g+m7SQICyXyqSUiibEWlqw+YaFI+kM9WbAe+jRFe2xBqbzTCLhP\nxCCsJbIZmv4Y9gDkuUPFWWb4ykdxIn1HPFJ3UBV1qJvVND2CQrFFJWoOpRTZjXnSqzcIwxZnMXpE\npUKK7aVrFHObrS5Kw6nK4SEA1xsin+rseTtKzxGom+4R0B6bGNWwo4eSJxnV9UYigj9wDr9X0t0b\nR0TLCwOwure9XcxvsXX7Gp41gWHEWFv7HtHRkbZfJqqUYuP296Do4LqjZLeXSds3GZx+oa3C556q\nI96f5VIaJxlrQWFOlw4sWJ8ufcdrzRAfu0gmN7tvWzY3R2zkfItK1Fqx4bPk8nd2biulyORn6Btr\nk9nVDZBamCUefQLbSWBaHjH/Ernl5WoDqI2l1+awgkEikTMYhoUXGcdlku3Fa60uWsNEBoYoFld2\nbisVUgjm8Qc6//OqewTq0zE9AiLyKeCfAibwZaXUKy0uUs+zHZ/k9FXSizdQgSCGIj55HtdPnrxz\nF/LiQzABmdWbEApiKQYuPI1pua0uWkOoMESVDTjw9DxngszGHeJD7RsuuJxJE7Gn9m0zLY9SFw/j\nxAbPkTUWyG1U35+GIwxfevGhVg20M8N1iV2tY7Lgd+qbLCgi/wHwc8CTwMtKqdePud+Rv2EiMgD8\na2AKuAV8TinV1LGqjmgIiIgJ/PfADwELwGsi8jWl1PutLZnmeAkGp19odTHahhcfqjYIeoFwZPKg\nMCzh2G3e+Dnmt6/be5j95CR+crLVxThVQaFI6oOWThZ8F/j3gX9+3B1O+A37EvBNpdQrIvKl2u2f\nbnyxd3VEQwB4GZhVSt0EEJHfAD4LHNsQuDo9wLf+5eebVDxN602//E9Cvv26iWlWv0qUUowMFvjl\nX/rxtj7TvH79JX7u5/8AMXdjHAfBBv/5j3+el19+voUl0x5HK7v4lVIfACe93x/0G/ZZ4JO1+30F\n+GN0Q+BIZ4D5PbcXgI+3qCyaptX83R/7D5Ff+SrffesO5bLiyuU+/tP/5K+3dSMA4MqVi/ydH32J\n3/43r7G8nGNw0OOH/9JHdCOgA5meS/yJOoYG3mhKHIEH/YaNKqUWa9eXgNFmFGivTmkIPBQR+SLw\nxdrNooi828rytLEhoLPXDDWWrp/jnVg3//Dnvvigf7etX/lndT+Eft88WEOCaczktv7w02/8Tj3j\ncZ6I7B3Xf1Up9ereO4jIN4Cjgi38rFLqd+s49j5KKSVyxHhbg3VKQ+AusHc90mRt2z61F+9VABF5\nXSn1cCGzeoyumwfT9XM8XTfH03XzYAd+bE+NUupTjXjcA8f4wTof4kG/YcsiMq6UWhSRcWDl0N4N\n1inLB18DLovItIg4wOeBr7W4TJqmaZr2MB70G/Y14Au1618ATq2H4WF1RENAKVUB/i7wh8AHwFeV\nUu+1tlSapmlarxORvyoiC8CfAf4PEfnD2vYJEfl9OPE37BXgh0RkBvjB2u3mPgfVpSGZROSLB8d5\ntCpdNw+m6+d4um6Op+vmwXT9tK+ubQhomqZpmnayjhga0DRN0zStMbquISAinxKRayIyW4vS1FNE\n5KyI/JGIvC8i74nIT9S2D4jI10VkpvY3uWefn6nV1zUR+YutK31ziIgpIt8Vkd+r3dZ1UyMi/SLy\nmyLyoYh8ICJ/RtdPlYj8vdpn6l0R+XUR8Xq5bkTkX4jIyt5l2o9THyLyMRF5p/a//07aPQhFF+qq\nhsCeMI6fBp4C/pqIPNXaUjVdBfgppdRTwCeAH6vVwf0wlpeBb9ZuU/vf54GngU8Bv1Krx272E1Qn\n7Nyn62bXPwX+QCn1BPAc1Xrq+foRkTPAjwMvKqWeoRov/vP0dt38z1Sf216PUx+/CvwocLl2afhy\nQG2/rmoIsCeMo1KqBNwP49gzlFKLSqk3a9fTVL/Iz1Cth6/U7vYV4K/Urn8W+A2lVFEpNQfMUq3H\nriQik8BfAr68Z7OuG0BE+oB/F/g1AKVUSSm1ha6f+ywgIiIWEAXu0cN1o5T6FrBxYPMj1Udt3XxC\nKfWnqjph7X/Zs4/WJN3WEDgqjOOZFpWl5URkCngB+DbHh7HstTr7J8A/APZGJ9d1UzUNrAL/U23o\n5Msi4qPrB6XUXeAXgTvAIrCtlPq/0HVz0KPWx5na9YPbtSbqtoaAViMiMeC3gJ9USqX2/q/W8u65\n5SIi8sPAilLqjePu06t1U2MBHwV+VSn1ApCl1rV7X6/WT22s+7NUG0sTgC8if2PvfXq1bo6j66Nz\ndFtD4KFCEXc7EbGpNgL+lVLqt2ubl2vdcBwIY9lLdfbngL8sIreoDht9v4j8S3Td3LcALCilvl27\n/ZtUGwa6fqqBXuaUUqtKqTLw28CfRdfNQY9aH3dr1w9u15qo2xoCPR+KuDbj9teAD5RSv7TnX8eF\nsfwa8HkRcUVkmupkne80q7zNpJT6GaXUpFJqiup74/9WSv0NdN0AoJRaAuZF5H5ymB+gmiZV1091\nSOATIhKtfcZ+gOr8G103+z1SfdSGEVIi8olavf4tWhBit+cppbrqAnwGuA7coJoZquVlavLz/z6q\n3XFvA2/VLp8BBqnO4p0BvgEM7NnnZ2v1dQ34dKufQ5Pq6ZPA79Wu67rZfb7PA6/X3j//Bkjq+tl5\nrv8Q+BB4F/hfAbeX6wb4darzJcpUe5N+5HHqA3ixVqc3gH9GLdCdvjTvoiMLapqmaVoP67ahAU3T\nNE3THoFuCGiapmlaD9MNAU3TNE3rYbohoGmapmk9TDcENE3TNK2H6YaAprUZqWaQnBORgdrtZO32\nVGtLpmlaN9INAU1rM0qpeaoZ2V6pbXoFeFUpdatlhdI0rWvpOAKa1oZqYaLfAP4F1RStz6tqaFtN\n07RTZbW6AJqmHaaUKovI3wf+APgLuhGgaVqj6KEBTWtfn6YawvWZVhdE07TupRsCmtaGROR54IeA\nTwB/735GN03TtNOmGwKa1mZqWdh+FfhJpdQd4B8Dv9jaUmma1q10Q0DT2s+PAneUUl+v3f4V4EkR\n+fdaWCZN07qUXjWgaZqmaT1M9whomqZpWg/TDQFN0zRN62G6IaBpmqZpPUw3BDRN0zSth+mGgKZp\nmqb1MN0Q0DRN07QephsCmqZpmtbDdENA0zRN03rY/w+/6kQnuQMixQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2483d10be80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "C = 1.0\n",
    "svm_linear = svm.SVC(kernel = 'linear',C = C, verbose = True)\n",
    "svm_linear.fit(train_X,train_Y[\"TPorosity\"]) \n",
    "plt = visualize_model(svm_linear,train_X[\"X\"],train_X[\"Y\"],train_Y[\"TPorosity\"],'Training Data and Suport Vector Machine Model')\n",
    "plot_svc_decision_function(svm_linear,plt,xmin,xmax,ymin,ymax, plot_support=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows the linear kernel support vector machine classification model (low porosity is red and high porosity is blue), the training dataset, margin outlined with dashed lines, and the resulting support vectors circled.  Note the support vectors impact the decision boundary as they are either in the margin, between the dashed lines, or misclassified outside the margin. It looks like we have quite a bit misclassication.\n",
    "\n",
    "Let's try a more complicated, flexible classifier with a polynomial kernel.  Recall, there is a trade off as more complexity reduces model bias (error due to a simple model not fitting the actual data), but increases model variance (sensitivity to training data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ii3BneX6os4/6PocrdexQqFtKuQB3lIstadTHCxyz4AJmLSBedT1OVyLsSKjb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\nYM65PT0pUcYI0wVQSHvO5dzFucmFHFMFHZQ5zVjjQ2fW4Zt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XSRECqXswogXDGcVksz/A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SYR7maoJ8SjD54m8pJreJQnATiQjHMgGUcVUOSw0p9IjqgQbvG5+MyutWwv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\nXZ0sWAv6MUVz33iF4pYTcAcRA79SyKZn9CnsLshyk5KzSO+lJ3GCHft8U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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2483c63ccf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "C = 0.000001\n",
    "svm_poly = svm.SVC(kernel = 'poly', degree = 2, max_iter = 100000000, C = C)\n",
    "svm_poly.fit(train_X,train_Y[\"TPorosity\"]) \n",
    "plt = visualize_model(svm_poly,train_X[\"X\"],train_X[\"Y\"],train_Y[\"TPorosity\"],'Training Data and Suport Vector Machine Model')\n",
    "plot_svc_decision_function(svm_poly,plt,xmin,xmax,ymin,ymax, plot_support=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model seems to work well for this problem.  The $2^{nd}$ order polynomial should have pretty low model variance.   Recall the \"C\" coefficient tunes the width of the margin; therefore, controls the number of support vectors / sample data the impact the model.  The dashed line above show a wide margin with a lot of points in the margin included as support vectors.  Let's try a dramatically higher \"C\" coefficient value.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pm27995\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=100000000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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1u2F0ikHAL3pKLPHSlP7ki3z1M1/mog+/i5e/9U11yzOZsFnRbWwEJoMRBAyz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\ne54dbpmTfgXLgq3Z1AWjBB6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kWhcTVgMef+pAo4sybsqVaPtYLWvDDNvdmeMt0dDc7CiX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\nRdEvkR6OBsIscjqmfP261unkKWeQ2pgHp1CQUlKvRFnsnd1V3Bb5NE5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ewpKbEAoyxXq8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kvACSAHvHejVAxcjPhkP4mJUaqqbqev69/5g49/i69+5p1c//Wf8vK3hy79BVeAIH+uLCaHxLNO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\nBwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2483d153d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "C = 1.0\n",
    "svm_poly = svm.SVC(kernel = 'poly', degree = 2, max_iter = 100000000, C = C)\n",
    "svm_poly.fit(train_X,train_Y[\"TPorosity\"]) \n",
    "plt = visualize_model(svm_poly,train_X[\"X\"],train_X[\"Y\"],train_Y[\"TPorosity\"],'Training Data and Suport Vector Machine Model')\n",
    "plot_svc_decision_function(svm_poly,plt,xmin,xmax,ymin,ymax, plot_support=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a \"C\" coefficient of 1.0 we now have much fewer support vectors, but the model is very similar.  Let's apply k-fold cross validation to tune the \"C\" coefficient.  We use the default of 3-fold cross validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pm27995\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=100000000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\pm27995\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=100000000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\pm27995\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=100000000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\pm27995\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=100000000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\pm27995\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=100000000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\pm27995\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=100000000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\pm27995\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=100000000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\pm27995\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=100000000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\pm27995\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=100000000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\pm27995\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=100000000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\pm27995\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=100000000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\pm27995\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=100000000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\pm27995\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=100000000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\pm27995\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=100000000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\pm27995\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=100000000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\pm27995\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=100000000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\pm27995\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=100000000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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W5r4EWBDbpxe2Z87WZg1tX7b203EdcQLOBkYBq+OWRYD1wJeALsAqogPzDSP6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x248382fa470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.learning_curve import validation_curve\n",
    "\n",
    "all_X = rand_sample.iloc[:,0:2].as_matrix()\n",
    "all_Y = rand_sample.iloc[:,2].as_matrix()\n",
    "\n",
    "C_range = np.transpose(np.c_[0.000001,0.00001,0.0001,0.001,0.01,0.1,1.0,10.0]) # the attempted \"C\" coefficients. \n",
    "\n",
    "train_score, val_score = validation_curve(svm_poly, all_X, all_Y,'C', C_range)\n",
    "\n",
    "plt.semilogx(C_range, np.median(train_score, 1), color='blue', label='training score')\n",
    "plt.semilogx(C_range, np.median(val_score, 1), color='red', label='validation score')\n",
    "plt.legend(loc='best')\n",
    "plt.ylim(0, 1)\n",
    "plt.xlabel('degree')\n",
    "plt.ylabel('score');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The validation score with the testing data and the training score are maximum with \"C\" coefficients around $10^{-6}$ to $10^{-5}$. We will retain that model.  We run it again below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "C = 0.000001\n",
    "svm_poly = svm.SVC(kernel = 'poly', degree = 2, max_iter = 100000000, C = C)\n",
    "svm_poly.fit(train_X,train_Y[\"TPorosity\"]) \n",
    "plt = visualize_model(svm_poly,train_X[\"X\"],train_X[\"Y\"],train_Y[\"TPorosity\"],'Training Data and Suport Vector Machine Model')\n",
    "plot_svc_decision_function(svm_poly,plt,xmin,xmax,ymin,ymax, plot_support=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets summarize the performance of our classification models, let's calculate and visualize confusion matrices for both the linear and tuned, polynomial kernel models for the training and the testing datasets.  Here's the result for the linear kernel for the training and testing datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[115  10]\n",
      " [  8 107]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x24839253b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[34  0]\n",
      " [ 7 19]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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OredRj4i4GXiuU++9gAvy3xcAezftDbQoHxM0M6uCvrXyRkmaWtN9TkScU8fr\nRkfE3Pz308DoPlQ4IDkEzcwqog8huCAitunPtCIiJEV/xjEQeHeomVlFqM5//TBP0hiA/P8zTSm8\nhbklaJXzxGOPcOyXPvtO9+yZT3DUvxzHAYf/U4lVWb2O3GFdth63Ei+99hZfv3oGAOuuOpzPbb8O\nw4a2MX/hG5x5yxMsevPtkiutlvTL8st8MlcDhwCn5/9/u8ynWHEOQauc8RtsxMV/vBWAxYsXM+kD\nm7LjrnuUXJXV66bHnmXyjPl88cPrvdPvyB3W4ZdT5/DgvIVM3HA1PjlhNJdOn9vDWAanfrbylh6X\ndBEwkXT8cBZwEin8LpV0OPAksG/TJtiiHIJWaVNuu5Fx663P2uPWLbsUq9OMea+wxorLLdVvzErD\neHDeQgDum/My3/r4mg7BLtR7DWA9ImL/bgbt3LSJDAA+JmiVNvl3V7Drnp8quwzrp1kvLGKbdVYG\n4APjV2H1TiFpS3aH1vOw5nEIVkRXd3cY7N584w1uvv4aPr7boL+UqeX99Lan2GXTUZy2xyYMHzqE\ntxYP+pMSu1DvaTFOwWby7tDqOB84E7iw5Doq47Ybr2PTzbdg9TXWLLsU66c5L73Oadc9BsCYlZZn\nq3ErlVxRBfXtOkFrErcEK6KbuzsMan+6+jfs+knvCh0IVhqWtrcF7PO+tbj+oQXlFlRRqvNhzeOW\nYAuRdARwBMBaY9cpuZpla9Grr3DnrX/h+NN+VHYp1kdHf3Q8m40ewchh7Zz1qQn8Zvpchg0dwi6b\njAJgylMvcuOj3t7rLB0TdMQVzSHYQvJtkc4B2Ox9Ww3ogyrDV1iRv0x/ouwyrAH/efMTXfb/44Pz\niy2kBTkCi+cQNDOrCqdg4RyCZmYV4TM/i+cTYyoi393hdmATSbPyHR3MbBDxdYLFc0uwInq4u4OZ\nDRYOuMI5BM3MKiBd/uAULJpD0MysCnyxfCkcgmZmFeEMLJ5D0MysKpyChXMImplVgm+OXQaHoJlZ\nRfiYYPEcgmZmFSAcgmVwCJqZVYR3hxbPIWhmVhFuCRbPIWhmVhHOwOI5BM3MqsC/mFsKh6CZWUX4\nmGDxHIJmZhXgs0PL4RA0M6sIZ2DxHIJmZlXhFCycQ9DMrCLamrg/VNITwMvAYuCtiNimaSMfQByC\nZmYVsQwagjtGxILmj3bgcAiamVWFd4cWrq3sAszMbMkvy9fzDxglaWrN44guRhnA9ZLu7ma44Zag\nmVk19O2VBejdAAAHLUlEQVSX5RfUcYzvwxExW9KawHWSZkTEzf2qcQByS9DMrCJU56MeETE7//8M\ncCWwXbPrHQgcgmZmVdGkFJS0oqSRHX8DuwD3L5OaW5x3h5qZVUJTf1l+NHCl0v7VduDXEfGnZo18\nIHEImplVgIC2JmVgRDwObNGcsQ1sDkEzs6rwJRKFcwiamVWEf0WieA5BM7OK8K9IFM8haGZWEc7A\n4jkEzcyqoG8Xy1uTOATNzCrDKVg0h6CZWQX4l+XL4RA0M6sIZ2DxHIJmZhXRzB/Vtfo4BM3MqsIZ\nWDiHoJlZRTgDi+cQNDOrAPkSiVI4BM3MKsK3TSueQ9DMrCqcgYVzCJqZVYQzsHgOQTOzivAxweI5\nBM3MKqGpvyxvdXIImplVgG+bVg6HoJlZRTgEi+cQNDOrCO8OLZ5D0MysCnyxfCkcgmZmFSB8iUQZ\nHIJmZlXhFCycQ9DMrCJ8TLB4bWUXYGZmScdNtHt71DcuTZL0kKRHJR27bCtvXQ5BM7OKaFYIShoC\nnAV8AtgM2F/SZsu2+tbkEDQzqwjV+a8O2wGPRsTjEfEGcDGw1zItvkU5BM3MKqDjjjFN2h06FphZ\n0z0r97NOfGJMi3rwvukLth6/8pNl11GAUcCCsouwfhssn+N6jb5w2rS7Jw8fqlF1Pn2YpKk13edE\nxDmNTnswcwi2qIhYo+waiiBpakRsU3Yd1j/+HHsXEZOaOLrZwDo13eNyP+vEu0PNzAaeu4CNJK0v\naTlgP+DqkmuqJLcEzcwGmIh4S9KXgMnAEOC8iHig5LIqySFoVefjHAODP8eCRcQ1wDVl11F1ioiy\nazAzMyuFjwmamdmg5RA0M7NByyFoZmaDlkPQKkfSsLJrsObI97A0qyyHoFWKpEnAqZImlF2LNU7S\nxgARsdhBaFXmELTKkPR+4ApgY2AvB2FrkrQHMF3Sr8FBaNXmSySsMnLobUy62e++wELgNx0X+UpS\neIGtNEkrApeTNmZ2ANoj4sA8bEhELC6zPrPOHIJWGZLaSSvN1yRtB3wKeJUUhPdLGhoRb5ZbpfVG\n0trAS8Aw4KfAax1BaFY1DkGrlNrWnqQPAn9P+kmYdfNjv4h4u8QSrQ8krU66W8yiiDhQ0tbAqxEx\no+TSzACHoFWMpLaIeFtSe77/4TrAL4H1gb0jYlrJJVofSRoFnAF8kHQfyx0jYla5VZklPjHGKiUH\n4I7AmZIETAC2BT7hAGxNEbEAuBdYBfh7B6BViUPQKkXShsC/Adfl3aL3A1v4DvitS9KqwG7ALhFx\nX9n1mNXy7lCrFElrAGtHxD0du0bLrsn6T9KwiHit7DrMOnMImpnZoOXdoWZmNmg5BM3MbNByCJqZ\n2aDlEDQzs0HLIWhmZoOWQ9DMzAYth6ANKpIWS5ou6X5Jl0laoR/jmijp9/nvPSUd28NzV5H0Tw1M\n42RJx9Tbv9Nzzpf0qT5Ma7yk+/tao1krcwjaYLMoIraMiM2BN4Cjagcq6fP3IiKujojTe3jKKkCf\nQ9DMli2HoA1mtwAb5hbQQ5IuJN2mbR1Ju0i6XdK03GIcASBpkqQZkqaRfuGC3P9QSWfmv0dLulLS\nPfmxA3A6sEFuhZ6Rn/d1SXdJulfSKTXjOl7Sw5JuBTbp7U1I+nwezz2SLu/Uuv2YpKl5fHvk5w+R\ndEbNtI/s74w0a1UOQRuU8m8XfgLouJflRsD/j4gJwCvACcDHImJrYCrwVUnDgJ8BnwTeD6zVzeh/\nAtwUEVsAWwMPAMcCj+VW6Ncl7ZKnuR2wJfB+SR+V9H5gv9xvN9LNw3tzRURsm6f3IHB4zbDxeRq7\nAz/N7+Fw4MWI2DaP//OS1q9jOmYDTnvZBZgVbLik6fnvW4BzgbWBJyPijtx/e2Az4Lb0QxYsB9wO\nbAr8LSIeAZD0S+CILqaxE3AwQP4l9RfzTaRr7ZIff83dI0ihOBK4MiJezdO4uo73tLmk75B2uY4A\nJtcMuzTff/URSY/n97AL8L6a44Ur52k/XMe0zAYUh6ANNosiYsvaHjnoXqntRfoVi/07PW+p1/WT\ngH+LiP/qNI2vNDCu80m/tXiPpEOBiTXDOt8cOPK0j46I2rBE0vgGpm3W0rw71Ozd7gA+lH/WCUkr\nStoYmAGMl7RBft7+3bz+BuAL+bVDJK0MvExq5XWYDBxWc6xxrKQ1gZuBvSUNlzSStOu1NyOBuZKG\nAgd0GvZpSW255vcAD+VpfyE/H0kbS1qxjumYDThuCZp1EhHzc4vqIknL594nRMTDko4A/iDpVdLu\n1JFdjOKfgXMkHQ4sBr4QEbdLui1fgvDHfFzwvcDtuSW6EDgwIqZJugS4B3gGuKuOkv8VuBOYn/+v\nrekpYAqwEnBURLwm6eekY4XT8g8Xzwf2rm/umA0s/iklMzMbtLw71MzMBi2HoJmZDVoOQTMzG7Qc\ngmZmNmg5BM3MbNByCJqZ2aDlEDQzs0Hr/wAFx7Dpf1WDpAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2483c7828d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_X = test.iloc[:,0:2]\n",
    "test_Y = test.iloc[:,2:3]\n",
    "\n",
    "train_linear_predict = svm_linear.predict(train_X)          # get the classifications for the training dataset\n",
    "cnf_train_linear_matrix = confusion_matrix(train_Y, train_linear_predict) # build the confusion matrix\n",
    "\n",
    "test_linear_predict = svm_linear.predict(test_X)            # get the classifications for the testing dataset\n",
    "cnf_test_linear_matrix = confusion_matrix(test_Y, test_linear_predict) # build the confusion matrix\n",
    "\n",
    "np.set_printoptions(precision=2)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_train_linear_matrix, classes=[-1,1],\n",
    "                      title='Confusion Matrix Train Dataset with Linear Kernel, without normalization')\n",
    "plt.show()\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_test_linear_matrix , classes=[-1,1],\n",
    "                      title='Confusion Matrix Test Dataset with Linear Kernel, without normalization')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the result for our best model, the tuned, polynomial kernel for the training and testing datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[119   6]\n",
      " [  9 106]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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V1HpwHfCHVu8/BzgzNxF+EfglS6/Wf5h0Hq7WaGBabsIaQ26ajYg7SevIT0jNZo+ztNm2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\n2Q7A6hnoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2483c862ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[33  1]\n",
      " [ 5 21]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2483d0dc668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_X = test.iloc[:,0:2]\n",
    "test_Y = test.iloc[:,2:3]\n",
    "\n",
    "train_poly_predict = svm_poly.predict(train_X)          # get the classifications for the training dataset\n",
    "cnf_train_poly_matrix = confusion_matrix(train_Y, train_poly_predict) # build the confusion matrix\n",
    "\n",
    "test_poly_predict = svm_poly.predict(test_X)            # get the classifications for the testing dataset\n",
    "cnf_test_poly_matrix = confusion_matrix(test_Y, test_poly_predict) # build the confusion matrix\n",
    "\n",
    "np.set_printoptions(precision=2)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_train_poly_matrix, classes=[-1,1],\n",
    "                      title='Confusion Matrix Train Dataset with Polynomial Kernel, without normalization')\n",
    "plt.show()\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_test_poly_matrix , classes=[-1,1],\n",
    "                      title='Confusion Matrix Test Dataset with Polynomial Kernel, without normalization')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can observe a minor improvement in performance for the tuned, polynomial kernel support vector machine over the untuned, simpler linear kernel model.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### More Exercises\n",
    "\n",
    "There are so many more exercises and tests that one could attempt to gain experience with support vector machines in Python. I'll end here for brevity, but I invite you to continue. Consider, on your own applying other data sets or attempting use of different kernels.  I hope you found this tutorial useful. I'm always happy to discuss geostatistics, statistical modeling, uncertainty modeling and machine learning,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "On twitter I'm the @GeostatsGuy.\n"
   ]
  }
 ],
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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